Updated on 2025/06/09

Information

 

写真a

 
KAMEI YASUTAKA
 
Organization
Faculty of Information Science and Electrical Engineering Department of Advanced Information Technology Professor
Joint Graduate School of Mathematics for Innovation (Concurrent)
School of Sciences Department of Physics(Concurrent)
School of Engineering Department of Electrical Engineering and Computer Science(Concurrent)
Graduate School of Information Science and Electrical Engineering Department of Information Science and Technology(Concurrent)
Title
Professor
Contact information
メールアドレス
Profile
Software Engineering, Software Metrics and Mining Software Repository
External link

Degree

  • Ph.D.

Research Interests・Research Keywords

  • Research theme: Software Engineering

    Keyword: Mining Software Repositories, Empirical Software Engineering, Software Evolution, Software Metrics, Software Test, Bug Prediction

    Research period: 2011.4

Awards

  • 善吾賞

    2023.3   ソフトウェアテスト技術振興協会   ミドルウェア製品開発に対する自動バグ修正技術の適用事例

  • SIGSE学生研究賞

    2023.3   情報処理学会 ソフトウェア工学研究会   Towards Robust Object Detection Models by Metamorphic Testing

  • SIGSS研究奨励賞

    2023.3   電子情報通信学会 ソフトウェアサイエンス研究会   確率的オートマトンとn-gramに基づくRNNに対するバグ限局

  • 末松安晴賞

    2022.6   電気情報通信学会   OSS開発プロジェクトの継続的進化を支える Just-In-Time バグ予測モデルの研究開発

  • SIGSS研究奨励賞

    2022.3   電子情報通信学会 ソフトウェアサイエンス研究会   Add トレースログを用いたバグ予測の性能評価

  • SIGSE学生研究賞

    2022.3   情報処理学会 ソフトウェア工学研究会   DVCリポジトリにおけるMLパイプラインの進化に関する調査

  • 学生奨励賞

    2021.9   SES2021   木編集距離を用いた類似コード検索器における深層学習モデルの性能評価

  • 研究奨励賞

    2021.9   SES2021   自動プログラム修正技術の性能評価 -九州大学の基幹教育データを用いた事例研究-

  • 特選論文

    2021.4   情報処理学会   READMEファイルの進化に関する実証的分析

  • SIGSE学生研究賞

    2021.3   情報処理学会 ソフトウェア工学研究会   RNNの抽象化モデルに対するバグ限局とその評価

  • 学生奨励賞

    2020.9   SES2020   コードレビューを通じたSelf-Admitted Technical Debtの追加・削除に関する実証的研究

  • 特選論文

    2020.4   情報処理学会   Revertに着目した不確かさに関する実証的分析

  • 日本ソフトウェア科学会第36回大会 学生奨励賞

    2019.8   日本ソフトウェア科学会   松井 健, 鵜林 尚靖, 佐藤 亮介, 亀井 靖高, ``敵対的サンプルに対するニューラルネットワークモデルの学習無し修正とその評価,''

  • SIGSE卓越研究賞

    2019.8   情報処理学会   Masanari Kondo, Cor-Paul Bezemer, Yasutaka Kamei, Ahmed E. Hassan, and Osamu Mizuno, ``The Impact of Feature Reduction Techniques on Defect Prediction Models,'' 情報処理学会 SIGSE卓越研究賞, August 2019

  • 最優秀論文賞

    2019.8   SES2019   村岡 北斗,鵜林 尚靖,亀井 靖高,佐藤 亮介, ``Revertに着目した不確かさに関する実証的分析,'' SES2019. 最優秀論文賞

  • 2018年度コンピュータサイエンス領域功績賞

    2019.3   情報処理学会   国際的研究活動活性化(ICSE勉強会)に対して.林 晋平, 小林 隆志, 渥美 紀寿, 石尾 隆, 亀井 靖高, 肥後 芳樹, 伏田 享平, 吉田 則裕

  • IPSJ/ACM Award for Early Career Contribution to Global Research

    2019.3   IPSJ/ACM   IPSJ/ACM Award for Early Career Contribution to Global Research: Yasutaka Kamei, ``Mining Software Repositories (MSR) to Improve Software Quality Assurance,''

  • Best Industry Paper Award

    2018.11   International Symposium on Empirical Software Engineering and Measurement (ESEM 2018)   Junji Shimagaki, Yasutaka Kamei, Abram Hindle, and Naoyasu Ubayashi, ``Automatic Topic Classification of Test Cases Using Text Mining at an Android Smartphone Vendor,'' International Symposium on Empirical Software Engineering and Measurement (ESEM 2018).

  • SIGSE卓越研究賞

    2017.9   情報処理学会   Shane Mcintosh,, 亀井 靖高, ``Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction,'' 情報処理学会 SIGSE卓越研究賞, 2017

  • 学生研究賞

    2017.3   情報処理学会 ソフトウェア工学研究会   中野 大扉, 松本 卓大, 山下 一寛, 亀井 靖高, 鵜林 尚靖, 高山 修一, 岩永 裕史, 岩崎 孝司, ``開発形態を考慮した企業内OSS事前品質評価手法,'' SIGSE学生研究賞, 2017

  • SIGSE卓越研究賞

    2016.9   情報処理学会   島垣 潤二, 亀井 靖高, Shane Mcintosh, Ahmed E. Hassan, 鵜林 尚靖, ``A Study of the Quality-Impacting Practices of Modern Code Review at Sony Mobile,'' 情報処理学会 SIGSE卓越研究賞, 2016

  • CS領域奨励賞

    2016.9   情報処理学会   深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, ``不確かさを包容した開発プロセスとその支援環境iArch-U,'' 情報処理学会CS領域奨励賞, 2016

  • 情報処理学会論文賞

    2016.6   情報処理学会   柏 祐太郎, 大平 雅雄, 阿萬 裕久, 亀井 靖高, ``大規模OSS開発における不具合修正時間の短縮化を目的としたバグトリアージ手法,'' 情報処理学会論文誌 2015年度 情報処理学会論文賞、2016

  • 学生研究賞

    2016.3   情報処理学会 ソフトウェア工学研究会   中村 隼也, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, ``LTSA連携による不確かさを包容した自動モデル検査,'' SIGSE学生研究賞, 2016

  • 学生研究賞

    2015.3   情報処理学会 ソフトウェア工学研究会   深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, ``不確かさを包容するJavaプログラミング環境,'' SIGSE 学生研究賞, 2015

  • 最優秀論文賞

    2014.9   SES2014   柏 祐太郎, 大平 雅雄, 阿萬 裕久, 亀井 靖高, ``大規模OSS開発における不具合修正時間の短縮化を目的としたバグトリアージ手法,'' SES2014. 最優秀論文賞

  • 特選論文賞

    2014.9   情報処理学会   柏 祐太郎, 大平 雅雄, 阿萬 裕久, 亀井 靖高, ``大規模OSS開発における不具合修正時間の短縮化を目的としたバグトリアージ手法,'' 情報処理学会論文誌 特選論文賞

  • CS領域奨励賞

    2014.9   情報処理学会   大坂 陽, 山下 一寛, 亀井 靖高, 鵜林 尚靖, ``リポジトリマイニングに対するHadoopの導入に向けた性能評価,'' 情報処理学会CS領域奨励賞, 2014

  • Distinguished Paper Award

    2014.6   International Working Conference on Mining Software Repositories (MSR 2014)   Shane Mcintosh, Yasutaka Kamei, Bram Adams and Ahmed E. Hassan, ``The Impact of Code Review Coverage and Code Review Participation on Software Quality: A Case Study of the Qt, VTK, and ITK Projects,'' International Working Conference on Mining Software Repositories (MSR 2014). (Distinguished Paper Award)

  • Young Author Award

    2013.12   IEEE Computer Society Japan Chapter   Yasutaka Kamei, Emad Shihab, Bram Adams, Ahmed E. Hassan, Audris Mockus, Anand Sinha, and Naoyasu Ubayashi, ``A Large-Scale Empirical Study of Just-In-Time Quality Assurance,'' IEEE Transactions on Software Engineering. Vol.39, No.6, pp.757-773, June, 2013.

  • 最優秀論文賞

    2013.9   SES2013   大坂 陽, 山下 一寛, 亀井 靖高, 鵜林 尚靖, ``リポジトリマイニングに対するHadoopの導入に向けた性能評価,'' SES2013. 最優秀論文賞

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Papers

  • The Human Side of Fuzzing: Challenges Faced by Developers During Fuzzing Activities

    Olivier Nourry, Yutaro Kashiwa, Bin Lin, Gabriele Bavota, Michele Lanza, Yasutaka Kamei

    ACM Transactions on Software Engineering and Methodology   2023.8

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    Fuzz testing, also known as fuzzing, is a software testing technique aimed at identifying software vulnerabilities. In recent decades, fuzzing has gained increasing popularity in the research community. However, existing studies led by fuzzing experts mainly focus on improving the coverage and performance of fuzzing techniques. That is, there is still a gap in empirical knowledge regarding fuzzing, especially about the challenges developers face when they adopt fuzzing. Understanding these challenges can provide valuable insights to both practitioners and researchers on how to further improve fuzzing processes and techniques. We conducted a study to understand the challenges encountered by developers during fuzzing. More specifically, we first manually analyzed 829 randomly sampled fuzzing-related GitHub issues and constructed a taxonomy consisting of 39 types of challenges (22 related to the fuzzing process itself, 17 related to using external fuzzing providers). We then surveyed 106 fuzzing practitioners to verify the validity of our taxonomy and collected feedback on how the fuzzing process can be improved. Our taxonomy, accompanied with representative examples and highlighted implications, can serve as a reference point on how to better adopt fuzzing techniques for practitioners, and indicates potential directions researchers can work on toward better fuzzing approaches and practices.

    DOI: 10.1145/3611668

  • Impact of Discretization Noise of the Dependent Variable on Machine Learning Classifiers in Software Engineering.

    Gopi Krishnan Rajbahadur, Shaowei Wang 0002, Yasutaka Kamei, Ahmed E. Hassan

    IEEE Transactions on Software Engineering   47 ( 7 )   1414 - 1430   2021.7

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    IEEE Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.

    DOI: 10.1109/TSE.2019.2924371

  • DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction

    Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi

    IEEE International Working Conference on Mining Software Repositories   2019-May   34 - 45   2019.5

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    © 2019 IEEE. Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction - aka. Just-In-Time (JIT) defect prediction - has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments on two popular software projects (i.e., QT and OPENSTACK) on three evaluation settings (i.e., cross-validation, short-period, and long-period) show that the best variant of DeepJIT (DeepJIT-Combined), compared with the best performing state-of-the-art approach, achieves improvements of 10.36-11.02% for the project QT and 9.51-13.69% for the project OPENSTACK in terms of the Area Under the Curve (AUC).

    DOI: 10.1109/MSR.2019.00016

  • DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction

    Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi

    IEEE International Working Conference on Mining Software Repositories   2019-May   34 - 45   2019.5

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    © 2019 IEEE. Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction - aka. Just-In-Time (JIT) defect prediction - has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments on two popular software projects (i.e., QT and OPENSTACK) on three evaluation settings (i.e., cross-validation, short-period, and long-period) show that the best variant of DeepJIT (DeepJIT-Combined), compared with the best performing state-of-the-art approach, achieves improvements of 10.36-11.02% for the project QT and 9.51-13.69% for the project OPENSTACK in terms of the Area Under the Curve (AUC).

    DOI: 10.1109/MSR.2019.00016

  • Automatic topic classification of test cases using text mining at an Android smartphone vendor

    Junji Shimagaki, Yasutaka Kamei, Naoyasu Ubayashi, Abram Hindle

    International Symposium on Empirical Software Engineering and Measurement   32 - 10   2018.10

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    © 2018 ACM. Background: An Android smartphone is an ecosystem of applications, drivers, operating system components, and assets. The volume of the software is large and the number of test cases needed to cover the functionality of an Android system is substantial. Enormous effort has been already taken to properly quantify "what features and apps were tested and verified?". This insight is provided by dashboards that summarize test coverage and results per feature. One method to achieve this is to manually tag or label test cases with the topic or function they cover, much like function points. At the studied Android smartphone vendor, tests are labelled with manually defined tags, so-called "feature labels (FLs)", and the FLs serve to categorize 100s to 1000s test cases into 10 to 50 groups. Aim: Unfortunately for developers, manual assignment of FLs to 1000s of test cases is a time consuming task, leading to inaccurately labeled test cases, which will render the dashboard useless. We created an automated system that suggests tags/labels to the developers for their test cases rather than manual labeling. Method: We use machine learning models to predict and label the functionality tested by 10,000 test cases developed at the company. Results: Through the quantitative experiments, our models achieved acceptable F-1 performance of 0.3 to 0.88. Also through the qualitative studies with expert teams, we showed that the hierarchy and path of tests was a good predictor of a feature's label. Conclusions: We find that this method can reduce tedious manual effort that software developers spent classifying test cases, while providing more accurate classification results.

    DOI: 10.1145/3239235.3268927

  • Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction Reviewed

    Shane McIntosh, Yasutaka Kamei

    IEEE Transactions on Software Engineering   44 ( 5 )   412 - 428   2018.5

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    Just-In-Time (JIT) models identify fix-inducing code changes. JIT models are trained using techniques that assume that past fix-inducing changes are similar to future ones. However, this assumption may not hold, e.g., as system complexity tends to accrue, expertise may become more important as systems age. In this paper, we study JIT models as systems evolve. Through a longitudinal case study of 37,524 changes from the rapidly evolving Qt and OpenStack systems, we find that fluctuations in the properties of fix-inducing changes can impact the performance and interpretation of JIT models. More specifically: (a) the discriminatory power (AUC) and calibration (Brier) scores of JIT models drop considerably one year after being trained; (b) the role that code change properties (e.g., Size, Experience) play within JIT models fluctuates over time; and (c) those fluctuations yield over- and underestimates of the future impact of code change properties on the likelihood of inducing fixes. To avoid erroneous or misleading predictions, JIT models should be retrained using recently recorded data (within three months). Moreover, quality improvement plans should be informed by JIT models that are trained using six months (or more) of historical data, since they are more resilient to period-specific fluctuations in the importance of code change properties.

    DOI: 10.1109/TSE.2017.2693980

  • Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding Reviewed

    Xiaochen Li, He Jiang, Yasutaka Kamei, Xin Chen

    IEEE Transactions on Software Engineering   2018.1

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    Developers increasingly rely on text matching tools to analyze the relation between natural language words and APIs. However, semantic gaps, namely textual mismatches between words and APIs, negatively affect these tools. Previous studies have transformed words or APIs into low-dimensional vectors for matching; however, inaccurate results were obtained due to the failure of modeling words and APIs simultaneously. To resolve this problem, two main challenges are to be addressed: the acquisition of massive words and APIs for mining and the alignment of words and APIs for modeling. Therefore, this study proposes Word2API to effectively estimate relatedness of words and APIs. Word2API collects millions of commonly used words and APIs from code repositories to address the acquisition challenge. Then, a shuffling strategy is used to transform related words and APIs into tuples to address the alignment challenge. Using these tuples, Word2API models words and APIs simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness estimation in terms of precision and NDCG. Word2API is also effective on solving typical software tasks, e.g., query expansion and API documents linking. A simple system with Word2API-expanded queries recommends up to 21.4% more related APIs for developers. Meanwhile, Word2API improves comparison algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to API documents.

    DOI: 10.1109/TSE.2018.2876006

  • The Impact Of Using Regression Models to Build Defect Classifiers Reviewed International journal

    Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan

    International Conference on Mining Software Repositories (MSR 2017)   2017.5

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  • Studying just-in-time defect prediction using cross-project models

    Yasutaka Kamei, Takafumi Fukushima, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi, Ahmed E. Hassan

    Empirical Software Engineering   21 ( 5 )   2072 - 2106   2016.10

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    © 2015, Springer Science+Business Media New York. Unlike traditional defect prediction models that identify defect-prone modules, Just-In-Time (JIT) defect prediction models identify defect-inducing changes. As such, JIT defect models can provide earlier feedback for developers, while design decisions are still fresh in their minds. Unfortunately, similar to traditional defect models, JIT models require a large amount of training data, which is not available when projects are in initial development phases. To address this limitation in traditional defect prediction, prior work has proposed cross-project models, i.e., models learned from other projects with sufficient history. However, cross-project models have not yet been explored in the context of JIT prediction. Therefore, in this study, we empirically evaluate the performance of JIT models in a cross-project context. Through an empirical study on 11 open source projects, we find that while JIT models rarely perform well in a cross-project context, their performance tends to improve when using approaches that: (1) select models trained using other projects that are similar to the testing project, (2) combine the data of several other projects to produce a larger pool of training data, and (3) combine the models of several other projects to produce an ensemble model. Our findings empirically confirm that JIT models learned using other projects are a viable solution for projects with limited historical data. However, JIT models tend to perform best in a cross-project context when the data used to learn them are carefully selected.

    DOI: 10.1007/s10664-015-9400-x

  • A Study of the Quality-Impacting Practices of Modern Code Review at Sony Mobile Reviewed International journal

    Junji Shimagaki, Yasutaka Kamei, Shane Mcintosh, Ahmed E. Hassan and Naoyasu Ubayashi

    the International Conference on Software Engineering (ICSE2016) Software Engineering in Practice (SEIP)   2016.5

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    Nowadays, a flexible, lightweight variant of the code review process (i.e., the practice of having other team members critique software changes) is adopted by open source and pro prietary software projects. While this flexibility is a blessing (e.g., enabling code reviews to span the globe), it does not mandate minimum review quality criteria like the formal code inspections of the past. Recent work shows that lax reviewing can impact the quality of open source systems. In this paper, we investigate the impact that code review- ing practices have on the quality of a proprietary system that is developed by Sony Mobile. We begin by replicating open source analyses of the relationship between software quality (as approximated by post-release defect-proneness) and: (1) code review coverage, i.e., the proportion of code changes that have been reviewed and (2) code review partic ipation, i.e., the degree of reviewer involvement in the code review process. We also perform a qualitative analysis, with a survey of 93 stakeholders, semi-structured interviews with 15 stakeholders, and a follow-up survey of 25 senior engineers. Our results indicate that while past measures of review coverage and participation do not share a relationship with defect-proneness at Sony Mobile, reviewing measures that are aware of the Sony Mobile development context are associated with defect-proneness. Our results have lead to improvements of the Sony Mobile code review process.

  • A study of the quality-impacting practices of modern code review at Sony mobile

    Junji Shimagaki, Yasutaka Kamei, Shane McIntosh, Ahmed E. Hassan, Naoyasu Ubayashi

    Proceedings - International Conference on Software Engineering   212 - 221   2016.5

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    © 2016 ACM. Nowadays, a flexible, lightweight variant of the code review process (i.e., the practice of having other team members critique software changes) is adopted by open source and proprietary software projects. While this flexibility is a blessing (e.g., enabling code reviews to span the globe), it does not mandate minimum review quality criteria like the formal code inspections of the past. Recent work shows that lax reviewing can impact the quality of open source systems. In this paper, we investigate the impact that code reviewing practices have on the quality of a proprietary system that is developed by Sony Mobile. We begin by replicating open source analyses of the relationship between software quality (as approximated by post-release defect-proneness) and: (1) code review coverage, i.e., the proportion of code changes that have been reviewed and (2) code review participation, i.e., the degree of reviewer involvement in the code review process. We also perform a qualitative analysis, with a survey of 93 stakeholders, semi-structured interviews with 15 stakeholders, and a follow-up survey of 25 senior engineers. Our results indicate that while past measures of review coverage and participation do not share a relationship with defect-proneness at Sony Mobile, reviewing measures that are aware of the Sony Mobile development context are associated with defect-proneness. Our results have lead to improvements of the Sony Mobile code review process.

    DOI: 10.1145/2889160.2889243

  • Defect Prediction: Accomplishments and Future Challenges Invited International journal

    Yasutaka Kamei, Emad Shihab

    Leaders of Tomorrow / Future of Software Engineering Track at International Conference on Software Analysis Evolution and Reengineering (SANER2016)   2016.3

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  • クラッシュレポートの送信頻度と不具合との関連付けに関する実証的評価 Reviewed

    小須田 光, 亀井 靖高, 鵜林 尚靖

    コンピュータソフトウェア   2015.12

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  • Studying Just-In-Time Defect Prediction using Cross-Project Models Reviewed International journal

    Yasutaka Kamei, Takafumi Fukushima, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi and Ahmed E. Hassan

    Journal of Empirical Software Engineering   2015.9

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  • An Empirical Study of the Impact of Modern Code Review Practices on Software Quality Reviewed International journal

    Shane Mcintosh, Yasutaka Kamei, Bram Adams and Ahmed E. Hassan

    Journal of Empirical Software Engineering   2015.5

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  • An Empirical Study of Just-In-Time Defect Prediction Using Cross-Project Models Reviewed International journal

    Takafumi Fukushima, Yasutaka Kamei, Shane McIntosh, Kazuhiro Yamashita and Naoyasu Ubayashi

    International Working Conference on Mining Software Repositories (MSR 2014)   2014.6

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  • The Impact of Code Review Coverage and Code Review Participation on Software Quality: A Case Study of the Qt, VTK, and ITK Projects Reviewed International journal

    Shane Mcintosh, Yasutaka Kamei, Bram Adams and Ahmed E. Hassan

    International Working Conference on Mining Software Repositories (MSR 2014)   2014.6

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  • Is Lines of Code a Good Measure of Effort in Effort-Aware Models? Reviewed International journal

    Emad Shihab, Yasutaka Kamei, Bram Adams, and Ahmed E. Hassan

    Information and Software Technology   2013.11

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  • Studying Re-opened Bugs in Open Source Software Reviewed International journal

    Emad Shihab, Akinori Ihara, Yasutaka Kamei, Walid M. Ibrahim, Masao Ohira, Bram Adams, Ahmed E. Hassan and Ken-ichi Matsumoto

    Journal of Empirical Software Engineering   2013.10

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    Language:English   Publishing type:Research paper (scientific journal)  

  • A Large-Scale Empirical Study of Just-In-Time Quality Assurance Reviewed International journal

    Yasutaka Kamei, Emad Shihab, Bram Adams, Ahmed E. Hassan, Audris Mockus, Anand Sinha and Naoyasu Ubayashi

    IEEE Transactions on Software Engineering   2013.6

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  • Revisiting Software Development Effort Estimation Based on Early Phase Development Activities Reviewed International journal

    Masateru Tsunoda, Koji Toda, Kyohei Fushida, Yasutaka Kamei, Meiyappan Nagappan and Naoyasu Ubayashi

    International Working Conference on Mining Software Repositories (MSR 2013)   2013.5

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  • グローバル環境下におけるOSS開発者の情報交換に対する時差の影響 Reviewed

    亀井 靖高, 大平 雅雄, 伊原 彰紀, 小山 貴和子, まつ本 真佑, 松本 健一, 鵜林 尚靖

    情報社会学会学会誌   2012.3

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  • ソフトウェア開発プロジェクトをまたがるfault-prone モジュール判別の試み ― 18 プロジェクトの実験から得た教訓 Reviewed

    藏本 達也, 亀井 靖高, 門田 暁人, 松本 健一

    電子情報通信学会論文誌   2012.3

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  • OSSプロジェクトにおける開発者の活動量を用いたコミッター候補者予測 Reviewed

    伊原 彰紀, 亀井 靖高, 大平 雅雄, 松本 健一, 鵜林 尚靖

    電子情報通信学会論文誌   2012.2

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  • An Empirical Study of Fault Prediction with Code Clone Metrics Reviewed International journal

    Yasutaka Kamei, Hiroki Sato, Akito Monden, Shinji Kawaguchi, Hidetake Uwano, Masataka Nagura, Ken-Ichi Matsumoto, Naoyasu Ubayashi

    The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011)   2011.11

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  • High-Impact Defects: A Study of Breakage and Surprise Defects Reviewed International journal

    Emad Shihab, Audris Mockus, Yasutaka Kamei, Bram Adams, Ahmed E. Hassan,

    the ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE2011)   2011.9

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  • An Empirical Study of Build Maintenance Effort Reviewed International journal

    Shane McIntosh, Bram Adams, Thanh H. D. Nguyen, Yasutaka Kamei and Ahmed E. Hassan

    the 33rd International Conference on Software Engineering (ICSE2011)   2011.5

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  • When conversations turn into work: a taxonomy of converted discussions and issues in GitHub.

    Dong Wang, Masanari Kondo, Yasutaka Kamei, Raula Gaikovina Kula, Naoyasu Ubayashi

    Empirical Software Engineering   28 ( 6 )   138 - 138   2023.11

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    DOI: 10.1007/s10664-023-10366-z

  • More than React: Investigating the Role of Emoji Reaction in GitHub Pull Requests.

    Dong Wang, Tao Xiao, Teyon Son, Raula Gaikovina Kula, Takashi Ishio, Yasutaka Kamei, Kenichi Matsumoto

    Empirical Software Engineering   28 ( 5 )   123 - 123   2023.10

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    DOI: 10.1007/s10664-023-10336-5

  • 実行経路を考慮した自動テストケース生成が自動プログラム修正に与える影響の分析

    松田 雄河, 山手 響介, 近藤 将成, 柏 祐太郎, 亀井 靖高, 鵜林 尚靖

    コンピュータ ソフトウェア   40 ( 1 )   1_45 - 1_56   2023.1

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    Analyzing the Impact of Automatic Test Case Generation Considering Execution Paths on Automated Program Repair
    For automated program repairs (APR), the cost of the patch generation process will be reduced if automatically-generated test suites can be used. Automatic test-case generation techniques often take classes as input. This study aims at identifying which classes should be given as input for the aforementioned technique. In this study, we investigate the relationship between the test suites that detected failures and the actual classes that had bugs fixed by developers. We observe the cases where test suites do not identify the classes fixed by developers as a cause of failures. We also find that these cases occur when the classes fixed by developers are on the traces generated by test suites' exercises. Based on this finding, we examine the impact of the automatically generated test-suites on the performance of APR. We demonstrate, taking into account all the classes exercised by the failed test cases, that the total number of generated patches decreases but the number of correct patches increases.

    DOI: 10.11309/jssst.40.1_45

  • プログラミング初学者のバグ修正履歴を用いたデバッグ問題自動生成の事例研究

    秋山 楽登, 中村 司, 近藤 将成, 亀井 靖高, 鵜林 尚靖

    コンピュータ ソフトウェア   39 ( 4 )   4_10 - 4_16   2022.10

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    A case study of automatic debugging problem generation using novice programmers' bug fix histories
    The debugging support for beginner programmers has been an active research area in recent years. However, instead of directly supporting their debugging, training such programmers to be intermediate programmers by using exercises to debug programs is overlooked. In this training, it is important to prepare the programs including bugs that capture the tendency of beginner programmers. Therefore, we focused on Learning-Mutation, which learns the bugs using machine translation from buggy programs and fixed programs, and automatically induces bugs into programs. In this study, we applied Learning-Mutation to the programs written by beginner programmers at Kyushu University. By comparing the induced bugs by Learning-Mutation with the actual bugs by such programmers, we evaluated whether Learning-Mutation can be used to support the exercises by preparing the programs including bugs. As a result, the induced bugs are similar to the actual bugs, and the patterns of bugs that are forgetting semicolons and undeclaring variables or functions accounted for more than 36% when the number of tokens was small. On the other hand, as the number of tokens increased, the number of incorrect expressions increased. Furthermore, although there are bugs that are difficult to generate, beam search relieves this difficulty.

    DOI: 10.11309/jssst.39.4_10

  • AIP: Scalable and Reproducible Execution Traces in Energy Studies on Mobile Devices

    Olivier Nourry, Yutaro Kashiwa, Bin Lin, Gabriele Bavota, Michele Lanza, Yasutaka Kamei

    2022 IEEE International Conference on Software Maintenance and Evolution (ICSME)   2022.10

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    DOI: 10.1109/icsme55016.2022.00057

  • An empirical study on self-admitted technical debt in modern code review.

    Yutaro Kashiwa, Ryoma Nishikawa, Yasutaka Kamei, Masanari Kondo, Emad Shihab, Ryosuke Sato, Naoyasu Ubayashi

    Inf. Softw. Technol.   146   106855 - 106855   2022.6

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    DOI: 10.1016/j.infsof.2022.106855

  • Do visual issue reports help developers fix bugs?

    Hiroki Kuramoto, Masanari Kondo, Yutaro Kashiwa, Yuta Ishimoto, Kaze Shindo, Yasutaka Kamei, Naoyasu Ubayashi

    Proceedings of the 30th IEEE/ACM International Conference on Program Comprehension   2022.5

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    DOI: 10.1145/3524610.3527882

  • コンテナ仮想化技術におけるSATDの削除に関する調査

    新堂 風, 近藤 将成, 柏 祐太郎, 東 英明, 柗本 真佑, 亀井 靖高, 鵜林 尚靖

    情報処理学会論文誌   63 ( 4 )   949 - 959   2022.4

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    Self-Admitted Technical Debt(SATD)とは,コード中に存在するバグや解消すべき課題のことであり,その中でも開発者が課題を認識したうえで,コードに埋め込んだものを指す.SATDの調査は,ソフトウェアの品質向上につながることから,SATDの追加や削除について様々な研究が行われている.他方,近年ソフトウェアのクラウド化にともない,コンテナ仮想化技術の1つであるDockerが注目されている.Dockerにおいても,従来のSATD研究で調査対象とされてきた一般的なプログラミング言語と同様に,SATDの存在が報告されている.しかし,DockerにおけるSATDの削除についての調査はまだ行われていない.SATD解決実態の把握により,SATD解決パターンの獲得や解決案の提示といった応用が期待できる.そこで本研究では,Docker Hubの人気上位250イメージを対象に,Dockerfileに含まれるSATDの削除の性質理解のための調査を行う.調査の結果,Dockerfile内のSATDのうち40.7%の負債が解決されており,存在期間は中央値が67日,平均値が166日であった.また,Dockerfile自体のレビューを求めるSATDや,外部システムに起因するSATDが多いことを明らかにした.外部システムに起因するSATDの早期解決を開発者に対して促すため,外部システムの変更を検知し,更新時に開発者に通知を行うツールを作成した.
    Developers occasionally leave bugs and issues that need to be resolved in source codes on purpose because of several reasons such as lack of development effort. Self-Admitted Technical Debt (SATD) refers to such bugs and issues. As it is pivotal to reduce SATDs from source codes, researchers have been investigating the additions and deletions of SATDs so far. A prior study found SATDs on Docker, one of the container virtualization technologies attracting attention in recent years. Although Docker is an important technology because of the shift to cloud computing, no prior work studies the deletions of SATD on Docker. Hence, in this study, we aim at revealing the process of deletions for SATDs on Docker to support the development with Docker. We investigate the characteristics of the deletions of SATDs on Dockerfiles, a text file to build an image for a container, in the top 250 most popular repositories of Docker Hub. We found that about 40.7% of the SATDs in the Dockerfiles are resolved within 67 days on median and 166 days on average. We also found that many SATDs exist that request a review of the Dockerfile itself and SATDs caused by external systems. In order to encourage developers to resolve SATDs caused by external systems as fast as possible, we created a tool that detects changes in external systems and notifies developers when such changes resolve the cause of SATDs.

    DOI: 10.20729/00217598

  • 木編集距離に着目した類似解答ソースコード検索器における深層学習モデルの性能評価

    沖野 健太郎, 松尾 春紀, 山本 大貴, 近藤 将成, 亀井 靖高, 鵜林 尚靖

    情報処理学会論文誌   63 ( 4 )   986 - 998   2022.4

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    近年のIT社会の発展によってIT人材の不足が深刻になり,プログラム自動生成を含むソフトウェア開発の自動化が求められている.多くの研究が行われているなかで,プログラム自動生成をより実用的なものとするために,自動生成の過程でソースコード検索器を使用している研究がある.その研究では,求めるソースコードに木構造が近いと推測される類似解答ソースコードを検索し,自動生成の雛形としている.この手法を用いることで,プログラミングコンテストAtCoderの解答ソースコードの自動生成において,検索を行わない場合と比較して自動生成できた件数が増加したと報告されている.本研究では,木編集距離を学習に用いたソースコード検索器に着目した.ソースコード検索器の性能に影響を与える要因を実証的に調査することで,プログラム自動生成の精度向上への知見を得ることを目指す.調査では,検索精度に影響を与える要因として,深層学習モデルの構造,ソースコードの入力形式,問題の複雑度の3つを対象とし,AtCoderの問題を使用して検索精度の比較を行った.調査の結果,類似解答ソースコード検索においてTransformerのエンコーダ部分の使用は有効であることが期待できること,AtCoderの問題に対して抽象構文木のベクトル表現の使用は有効であるとはいえないこと,問題の複雑度は検索精度に影響を与えることを示した.
    Automatic program generation is an active research topic in software engineering. To make automatic program generation more practical, a prior study applies source code search to the method of automatic program generation. In that study, source codes whose tree structures may be close to the desired source code developers require are searched and used as a template for the method. They reported that the method with source code search increases the number of generated source codes compared to the method without source code search. In this study, we use source code search using the tree edit distance. By empirically investigating the factors that affect the performance of source code search, we aim to improve the accuracy of automatic program generation. We focused on three factors that affect the search accuracy: the structure of the deep learning model, the input format of the source code, and the complexity of the problem. We compared the search accuracy on the AtCoder problems. We found that the encoder part of Transformer is promising for source code search, the use of vectorized representation of abstract syntax trees is less effective for the AtCoder problems, and the complexity of the problem affects the search accuracy.

    DOI: 10.20729/00217602

  • An empirical study on self-admitted technical debt in Dockerfiles.

    Hideaki Azuma, Shinsuke Matsumoto, Yasutaka Kamei, Shinji Kusumoto

    Empirical Software Engineering   27 ( 2 )   49 - 49   2022.1

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    DOI: 10.1007/s10664-021-10081-7

  • Does shortening the release cycle affect refactoring activities: A case study of the JDT Core, Platform SWT, and UI projects.

    Olivier Nourry, Yutaro Kashiwa, Yasutaka Kamei, Naoyasu Ubayashi

    Information & Software Technology   139   106623 - 106623   2021.11

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    DOI: 10.1016/j.infsof.2021.106623

  • Studying donations and their expenses in open source projects: a case study of GitHub projects collecting donations through open collectives.

    Jiayuan Zhou, Shaowei Wang 0002, Yasutaka Kamei, Ahmed E. Hassan, Naoyasu Ubayashi

    Empirical Software Engineering   27 ( 1 )   24 - 24   2021.11

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    DOI: 10.1007/s10664-021-10060-y

  • Does Refactoring Break Tests and to What Extent?

    Yutaro Kashiwa, Kazuki Shimizu, Bin Lin 0008, Gabriele Bavota, Michele Lanza, Yasutaka Kamei, Naoyasu Ubayashi

    ICSME   171 - 182   2021.10

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    DOI: 10.1109/ICSME52107.2021.00022

  • PYREF: Refactoring Detection in Python Projects.

    Hassan Atwi, Bin Lin 0008, Nikolaos Tsantalis, Yutaro Kashiwa, Yasutaka Kamei, Naoyasu Ubayashi, Gabriele Bavota, Michele Lanza

    SCAM   136 - 141   2021.9

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    DOI: 10.1109/SCAM52516.2021.00025

  • An empirical study on the use of SZZ for identifying inducing changes of non-functional bugs.

    Sophia Quach, Maxime Lamothe, Yasutaka Kamei, Weiyi Shang

    Empirical Software Engineering   26 ( 4 )   71 - 71   2021.7

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    DOI: 10.1007/s10664-021-09970-8

  • Evaluating the impact of falsely detected performance bug-inducing changes in JIT models.

    Sophia Quach, Maxime Lamothe, Bram Adams, Yasutaka Kamei, Weiyi Shang

    Empirical Software Engineering   26 ( 5 )   97 - 97   2021.7

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    DOI: 10.1007/s10664-021-10004-6

  • READMEファイルの進化に関する実証的分析

    亀井靖高, 清水一輝, 柏祐太郎, 佐藤亮介, 鵜林尚靖

    情報処理学会論文誌ジャーナル(Web)   62 ( 4 )   2021.4

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    An Empirical Analysis of the Evolution of README Files

  • Leveraging Fault Localisation to Enhance Defect Prediction. Reviewed International journal

    Jeongju Sohn, Yasutaka Kamei, Shane McIntosh, Shin Yoo

    2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)   2021.3

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  • Leveraging Fault Localisation to Enhance Defect Prediction.

    Jeongju Sohn, Yasutaka Kamei, Shane McIntosh, Shin Yoo

    28th IEEE International Conference on Software Analysis, Evolution and Reengineering(SANER)   284 - 294   2021.3

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    DOI: 10.1109/SANER50967.2021.00034

  • The impact of feature importance methods on the interpretation of defect classifiers Reviewed

    Gopi Krishnan Rajbahadur, Shaowei Wang, Gustavo Ansaldi, Yasutaka Kamei, Ahmed E. Hassan

    IEEE Transactions on Software Engineering   2021.2

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    Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. 2) The computed feature importance ranks by the studied CA methods exhibit a strong agreement including the features reported at top-1 and top-3 ranks for a given dataset and classifier, while even the commonly used CS methods yield vastly different feature importance ranks. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods.

    DOI: 10.1109/TSE.2021.3056941

  • The impact of feature importance methods on the interpretation of defect classifiers

    Gopi Krishnan Rajbahadur, Shaowei Wang, Gustavo Ansaldi, Yasutaka Kamei, Ahmed E. Hassan

    IEEE Transactions on Software Engineering   2021.2

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    Classifier specific (CS) and classifier agnostic (CA) feature importance methods are widely used (often interchangeably) by prior studies to derive feature importance ranks from a defect classifier. However, different feature importance methods are likely to compute different feature importance ranks even for the same dataset and classifier. Hence such interchangeable use of feature importance methods can lead to conclusion instabilities unless there is a strong agreement among different methods. Therefore, in this paper, we evaluate the agreement between the feature importance ranks associated with the studied classifiers through a case study of 18 software projects and six commonly used classifiers. We find that: 1) The computed feature importance ranks by CA and CS methods do not always strongly agree with each other. 2) The computed feature importance ranks by the studied CA methods exhibit a strong agreement including the features reported at top-1 and top-3 ranks for a given dataset and classifier, while even the commonly used CS methods yield vastly different feature importance ranks. Such findings raise concerns about the stability of conclusions across replicated studies. We further observe that the commonly used defect datasets are rife with feature interactions and these feature interactions impact the computed feature importance ranks of the CS methods (not the CA methods). We demonstrate that removing these feature interactions, even with simple methods like CFS improves agreement between the computed feature importance ranks of CA and CS methods. In light of our findings, we provide guidelines for stakeholders and practitioners when performing model interpretation and directions for future research, e.g., future research is needed to investigate the impact of advanced feature interaction removal methods on computed feature importance ranks of different CS methods.

    DOI: 10.1109/TSE.2021.3056941

  • How Fast and Effectively Can Code Change History Enrich Stack Overflow?

    Ryujiro Nishinaka, Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato

    Proceedings - IEEE International Conference on Software Quality, Reliability and Security, QRS 2020   467 - 478   2020.12

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    DOI: 10.1109/QRS51102.2020.00066

  • Guest editorial Mining software repositories 2018 Reviewed

    Yasutaka Kamei, Andy Zaidman

    Empirical Software Engineering   25 ( 3 )   2055 - 2057   2020.5

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    DOI: 10.1007/s10664-020-09817-8

  • Revertに着目した不確かさに関する実証的分析 Reviewed

    村岡 北斗, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    情報処理学会論文誌   2020.4

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  • The impact of feature reduction techniques on defect prediction models.

    Masanari Kondo, Cor-Paul Bezemer, Yasutaka Kamei, Ahmed E. Hassan, Osamu Mizuno

    Empirical Software Engineering   24 ( 4 )   1925 - 1963   2019.8

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    © 2019, Springer Science+Business Media, LLC, part of Springer Nature. Defect prediction is an important task for preserving software quality. Most prior work on defect prediction uses software features, such as the number of lines of code, to predict whether a file or commit will be defective in the future. There are several reasons to keep the number of features that are used in a defect prediction model small. For example, using a small number of features avoids the problem of multicollinearity and the so-called ‘curse of dimensionality’. Feature selection and reduction techniques can help to reduce the number of features in a model. Feature selection techniques reduce the number of features in a model by selecting the most important ones, while feature reduction techniques reduce the number of features by creating new, combined features from the original features. Several recent studies have investigated the impact of feature selection techniques on defect prediction. However, there do not exist large-scale studies in which the impact of multiple feature reduction techniques on defect prediction is investigated. In this paper, we study the impact of eight feature reduction techniques on the performance and the variance in performance of five supervised learning and five unsupervised defect prediction models. In addition, we compare the impact of the studied feature reduction techniques with the impact of the two best-performing feature selection techniques (according to prior work). The following findings are the highlights of our study: (1) The studied correlation and consistency-based feature selection techniques result in the best-performing supervised defect prediction models, while feature reduction techniques using neural network-based techniques (restricted Boltzmann machine and autoencoder) result in the best-performing unsupervised defect prediction models. In both cases, the defect prediction models that use the selected/generated features perform better than those that use the original features (in terms of AUC and performance variance). (2) Neural network-based feature reduction techniques generate features that have a small variance across both supervised and unsupervised defect prediction models. Hence, we recommend that practitioners who do not wish to choose a best-performing defect prediction model for their data use a neural network-based feature reduction technique.

    DOI: 10.1007/s10664-018-9679-5

  • When and Why Do Software Developers Face Uncertainty?

    Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato

    19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019 Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019   288 - 299   2019.7

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    Recently, many developers begin to notice that uncertainty is a crucial problem in software development. Unfortunately, no one knows how often uncertainty appears or what kinds of uncertainty exist in actual projects, because there are no empirical studies on uncertainty. To deal with this problem, we conduct a large-scale empirical study analyzing commit messages and revision histories of 1,444 OSS projects randomly selected from the GitHub repositories. The main findings are as follows: 1) Uncertainty exists in the ratio of 1.44% (average); 2) Uncertain program behavior, uncertain variable/value/name, and uncertain program defects are major kinds of uncertainty; and 3) Sometimes developers tend to take an action for not resolving but escaping or ignoring uncertainty. Uncertainty exists everywhere in a certain percentage and developers cannot ignore the existence of uncertainty.

    DOI: 10.1109/QRS.2019.00045

  • When and Why Do Software Developers Face Uncertainty?

    Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato

    Proceedings - 19th IEEE International Conference on Software Quality, Reliability and Security, QRS 2019   288 - 299   2019.7

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    © 2019 IEEE. Recently, many developers begin to notice that uncertainty is a crucial problem in software development. Unfortunately, no one knows how often uncertainty appears or what kinds of uncertainty exist in actual projects, because there are no empirical studies on uncertainty. To deal with this problem, we conduct a large-scale empirical study analyzing commit messages and revision histories of 1,444 OSS projects randomly selected from the GitHub repositories. The main findings are as follows: 1) Uncertainty exists in the ratio of 1.44% (average); 2) Uncertain program behavior, uncertain variable/value/name, and uncertain program defects are major kinds of uncertainty; and 3) Sometimes developers tend to take an action for not resolving but escaping or ignoring uncertainty. Uncertainty exists everywhere in a certain percentage and developers cannot ignore the existence of uncertainty.

    DOI: 10.1109/QRS.2019.00045

  • A survey of self-admitted technical debt Reviewed

    Giancarlo Sierra, Emad Shihab, Yasutaka Kamei

    Journal of Systems and Software   152   70 - 82   2019.6

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    Technical Debt is a metaphor used to express sub-optimal source code implementations that are introduced for short-term benefits that often need to be paid back later, at an increased cost. In recent years, various empirical studies have focused on investigating source code comments that indicate Technical Debt often referred to as Self-Admitted Technical Debt (SATD). Since the introduction of SATD as a concept, an increasing number of studies have examined various aspects pertaining to SATD. Therefore, in this paper we survey research work on SATD, analyzing the characteristics of current approaches and techniques for SATD detection, comprehension, and repayment. To motivate the submission of novel and improved work, we compile tools, resources, and data sets made available to replicate or extend current SATD research. To set the stage for future work, we identify open challenges in the study of SATD, areas that are missing investigation, and discuss potential future research avenues.

    DOI: 10.1016/j.jss.2019.02.056

  • A survey of self-admitted technical debt.

    Giancarlo Sierra, Emad Shihab, Yasutaka Kamei

    Journal of Systems and Software   152   70 - 82   2019.6

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    © 2019 Elsevier Inc. Technical Debt is a metaphor used to express sub-optimal source code implementations that are introduced for short-term benefits that often need to be paid back later, at an increased cost. In recent years, various empirical studies have focused on investigating source code comments that indicate Technical Debt often referred to as Self-Admitted Technical Debt (SATD). Since the introduction of SATD as a concept, an increasing number of studies have examined various aspects pertaining to SATD. Therefore, in this paper we survey research work on SATD, analyzing the characteristics of current approaches and techniques for SATD detection, comprehension, and repayment. To motivate the submission of novel and improved work, we compile tools, resources, and data sets made available to replicate or extend current SATD research. To set the stage for future work, we identify open challenges in the study of SATD, areas that are missing investigation, and discuss potential future research avenues.

    DOI: 10.1016/j.jss.2019.02.056

  • Towards effective AI-powered agile project management

    Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose, Yasutaka Kamei

    Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2019   41 - 44   2019.5

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    © 2019 IEEE. The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects, e.g. customers' needs, developers' performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.

    DOI: 10.1109/ICSE-NIER.2019.00019

  • GreenBundle: An Empirical Study on the Energy Impact of Bundled Processing

    Shaiful Alam Chowdhury, Abram Hindle, Rick Kazman, Takumi Shuto, Ken Matsui, Yasutaka Kamei

    Proceedings - International Conference on Software Engineering   2019-May   1107 - 1118   2019.5

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    © 2019 IEEE. Energy consumption is a concern in the data-center and at the edge, on mobile devices such as smartphones. Software that consumes too much energy threatens the utility of the end-user's mobile device. Energy consumption is fundamentally a systemic kind of performance and hence it should be addressed at design time via a software architecture that supports it, rather than after release, via some form of refactoring. Unfortunately developers often lack knowledge of what kinds of designs and architectures can help address software energy consumption. In this paper we show that some simple design choices can have significant effects on energy consumption. In particular we examine the Model-View-Controller architectural pattern and demonstrate how converting to Model-View-Presenter with bundling can improve the energy performance of both benchmark systems and real world applications. We show the relationship between energy consumption and bundled and delayed view updates: bundling events in the presenter can often reduce energy consumption by 30%.

    DOI: 10.1109/ICSE.2019.00114

  • Git-based integrated uncertainty manager

    Naoyasu Ubayashi, Takuya Watanabe, Yasutaka Kamei, Ryosuke Sato

    Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering: Companion, ICSE-Companion 2019   95 - 98   2019.5

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    © 2019 IEEE. Nowadays, many software systems are required to be updated and delivered in a short period of time. It is important for developers to make software embrace uncertainty, because user requirements or design decisions are not always completely determined. This paper introduces iArch-U, an Eclipse-based uncertainty-aware software development tool chain, for developers to properly describe, trace, and manage uncertainty crosscutting over UML modeling, Java programming, and testing phases. Integrating with Git, iArch-U can manage why/when/where uncertain concerns arise or are fixed to be certain in a project. In this tool demonstration, we show the world of uncertainty-aware software development using iArch-U. Our tool is open source software released from http://posl.github.io/iArch/.

    DOI: 10.1109/ICSE-Companion.2019.00047

  • Towards effective AI-powered agile project management

    Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose, Yasutaka Kamei

    41st IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results, ICSE-NIER 2019 Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering New Ideas and Emerging Results, ICSE-NIER 2019   41 - 44   2019.5

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    Language:English   Publishing type:Research paper (other academic)  

    The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects, e.g. customers' needs, developers' performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry.

    DOI: 10.1109/ICSE-NIER.2019.00019

  • GreenBundle: An Empirical Study on the Energy Impact of Bundled Processing

    Shaiful Alam Chowdhury, Abram Hindle, Rick Kazman, Takumi Shuto, Ken Matsui, Yasutaka Kamei

    Proceedings - International Conference on Software Engineering   2019-May   1107 - 1118   2019.5

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    Language:English   Publishing type:Research paper (other academic)  

    © 2019 IEEE. Energy consumption is a concern in the data-center and at the edge, on mobile devices such as smartphones. Software that consumes too much energy threatens the utility of the end-user's mobile device. Energy consumption is fundamentally a systemic kind of performance and hence it should be addressed at design time via a software architecture that supports it, rather than after release, via some form of refactoring. Unfortunately developers often lack knowledge of what kinds of designs and architectures can help address software energy consumption. In this paper we show that some simple design choices can have significant effects on energy consumption. In particular we examine the Model-View-Controller architectural pattern and demonstrate how converting to Model-View-Presenter with bundling can improve the energy performance of both benchmark systems and real world applications. We show the relationship between energy consumption and bundled and delayed view updates: bundling events in the presenter can often reduce energy consumption by 30%.

    DOI: 10.1109/ICSE.2019.00114

  • Git-based integrated uncertainty manager

    Naoyasu Ubayashi, Takuya Watanabe, Yasutaka Kamei, Ryosuke Sato

    41st IEEE/ACM International Conference on Software Engineering: Companion, ICSE-Companion 2019 Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering Companion, ICSE-Companion 2019   95 - 98   2019.5

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    Nowadays, many software systems are required to be updated and delivered in a short period of time. It is important for developers to make software embrace uncertainty, because user requirements or design decisions are not always completely determined. This paper introduces iArch-U, an Eclipse-based uncertainty-aware software development tool chain, for developers to properly describe, trace, and manage uncertainty crosscutting over UML modeling, Java programming, and testing phases. Integrating with Git, iArch-U can manage why/when/where uncertain concerns arise or are fixed to be certain in a project. In this tool demonstration, we show the world of uncertainty-aware software development using iArch-U. Our tool is open source software released from http://posl.github.io/iArch/.

    DOI: 10.1109/ICSE-Companion.2019.00047

  • IARCH-U/MC An uncertainty-aware model checker for embracing known unknowns

    Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato

    13th International Conference on Software Technologies, ICSOFT 2018 ICSOFT 2018 - Proceedings of the 13th International Conference on Software Technologies   176 - 184   2019.1

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    Embracing uncertainty in software development is one of the crucial research topics in software engineering. In most projects, we have to deal with uncertain concerns by using informal ways such as documents, mailing lists, or issue tracking systems. This task is tedious and error-prone. Especially, uncertainty in programming is one of the challenging issues to be tackled, because it is difficult to verify the correctness of a program when there are uncertain user requirements, unfixed design choices, and alternative algorithms. This paper proposes iArch-U/MC, an uncertainty-aware model checker for verifying whether or not some important properties are guaranteed even if Known Unknowns remain in a program. Our tool is based on LTSA (Labelled Transition System Analyzer) and is implemented as an Eclipse plug-in.

  • The impact of feature reduction techniques on defect prediction models Reviewed

    Masanari Kondo, Cor Paul Bezemer, Yasutaka Kamei, Ahmed E. Hassan, Osamu Mizuno

    Empirical Software Engineering   2019.1

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    Defect prediction is an important task for preserving software quality. Most prior work on defect prediction uses software features, such as the number of lines of code, to predict whether a file or commit will be defective in the future. There are several reasons to keep the number of features that are used in a defect prediction model small. For example, using a small number of features avoids the problem of multicollinearity and the so-called ‘curse of dimensionality’. Feature selection and reduction techniques can help to reduce the number of features in a model. Feature selection techniques reduce the number of features in a model by selecting the most important ones, while feature reduction techniques reduce the number of features by creating new, combined features from the original features. Several recent studies have investigated the impact of feature selection techniques on defect prediction. However, there do not exist large-scale studies in which the impact of multiple feature reduction techniques on defect prediction is investigated. In this paper, we study the impact of eight feature reduction techniques on the performance and the variance in performance of five supervised learning and five unsupervised defect prediction models. In addition, we compare the impact of the studied feature reduction techniques with the impact of the two best-performing feature selection techniques (according to prior work). The following findings are the highlights of our study: (1) The studied correlation and consistency-based feature selection techniques result in the best-performing supervised defect prediction models, while feature reduction techniques using neural network-based techniques (restricted Boltzmann machine and autoencoder) result in the best-performing unsupervised defect prediction models. In both cases, the defect prediction models that use the selected/generated features perform better than those that use the original features (in terms of AUC and performance variance). (2) Neural network-based feature reduction techniques generate features that have a small variance across both supervised and unsupervised defect prediction models. Hence, we recommend that practitioners who do not wish to choose a best-performing defect prediction model for their data use a neural network-based feature reduction technique.

    DOI: 10.1007/s10664-018-9679-5

  • Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering Reviewed

    Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan

    IEEE Transactions on Software Engineering   2019.1

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    Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.

    DOI: 10.1109/TSE.2019.2924371

  • Studying the Cost and Effectiveness of OSS Quality Assessment Models: An Experience Report of Fujitsu QNET

    Yasutaka Kamei, Takahiro Matsumoto, Kazuhiro Yamashita, Naoyasu Ubayashi, Takashi Iwasaki, Shuichi Takayama

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E101D ( 11 )   2744 - 2753   2018.11

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    Nowadays, open source software (OSS) systems are adopted by proprietary software projects. To reduce the risk of using problematic OSS systems (e.g., causing system crashes), it is important for proprietary software projects to assess OSS systems in advance. Therefore, OSS quality assessment models are studied to obtain information regarding the quality of OSS systems. Although the OSS quality assessment models are partially validated using a small number of case studies, to the best of our knowledge, there are few studies that empirically report how industrial projects actually use OSS quality assessment models in their own development process. In this study, we empirically evaluate the cost and effectiveness of OSS quality assessment models at Fujitsu Kyushu Network Technologies Limited (Fujitsu QNET). To conduct the empirical study, we collect datasets from (a) 120 OSS projects that Fujitsu QNET's projects actually used and (b) 10 problematic OSS projects that caused major problems in the projects. We find that (1) it takes average and median times of 51 and 49 minutes, respectively, to gather all assessment metrics per OSS project and (2) there is a possibility that we can filter problematic OSS systems by using the threshold derived from a pool of assessment metrics. Fujitsu QNET's developers agree that our results lead to improvements in Fujitsu QNET's OSS assessment process. We believe that our work significantly contributes to the empirical knowledge about applying OSS assessment techniques to industrial projects.

    DOI: 10.1587/transinf.2018EDP7163

  • Studying the Cost and Effectiveness of OSS Quality Assessment Models: An Experience Report of Fujitsu QNET

    Yasutaka Kamei, Takahiro Matsumoto, Kazuhiro Yamashita, Naoyasu Ubayashi, Takashi Iwasaki, Shuichi Takayama

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E101D ( 11 )   2744 - 2753   2018.11

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    Nowadays, open source software (OSS) systems are adopted by proprietary software projects. To reduce the risk of using problematic OSS systems (e.g., causing system crashes), it is important for proprietary software projects to assess OSS systems in advance. Therefore, OSS quality assessment models are studied to obtain information regarding the quality of OSS systems. Although the OSS quality assessment models are partially validated using a small number of case studies, to the best of our knowledge, there are few studies that empirically report how industrial projects actually use OSS quality assessment models in their own development process. In this study, we empirically evaluate the cost and effectiveness of OSS quality assessment models at Fujitsu Kyushu Network Technologies Limited (Fujitsu QNET). To conduct the empirical study, we collect datasets from (a) 120 OSS projects that Fujitsu QNET's projects actually used and (b) 10 problematic OSS projects that caused major problems in the projects. We find that (1) it takes average and median times of 51 and 49 minutes, respectively, to gather all assessment metrics per OSS project and (2) there is a possibility that we can filter problematic OSS systems by using the threshold derived from a pool of assessment metrics. Fujitsu QNET's developers agree that our results lead to improvements in Fujitsu QNET's OSS assessment process. We believe that our work significantly contributes to the empirical knowledge about applying OSS assessment techniques to industrial projects.

    DOI: 10.1587/transinf.2018EDP7163

  • Automatic topic classification of test cases using text mining at an Android smartphone vendor

    Junji Shimagaki, Yasutaka Kamei, Naoyasu Ubayashi, Abram Hindle

    12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2018 Proceedings of the 12th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2018   2018.10

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    Background: An Android smartphone is an ecosystem of applications, drivers, operating system components, and assets. The volume of the software is large and the number of test cases needed to cover the functionality of an Android system is substantial. Enormous effort has been already taken to properly quantify "what features and apps were tested and verified?". This insight is provided by dashboards that summarize test coverage and results per feature. One method to achieve this is to manually tag or label test cases with the topic or function they cover, much like function points. At the studied Android smartphone vendor, tests are labelled with manually defined tags, so-called "feature labels (FLs)", and the FLs serve to categorize 100s to 1000s test cases into 10 to 50 groups. Aim: Unfortunately for developers, manual assignment of FLs to 1000s of test cases is a time consuming task, leading to inaccurately labeled test cases, which will render the dashboard useless. We created an automated system that suggests tags/labels to the developers for their test cases rather than manual labeling. Method: We use machine learning models to predict and label the functionality tested by 10,000 test cases developed at the company. Results: Through the quantitative experiments, our models achieved acceptable F-1 performance of 0.3 to 0.88. Also through the qualitative studies with expert teams, we showed that the hierarchy and path of tests was a good predictor of a feature's label. Conclusions: We find that this method can reduce tedious manual effort that software developers spent classifying test cases, while providing more accurate classification results.

    DOI: 10.1145/3239235.3268927

  • Cross-Validation-Based Association Rule Prioritization Metric for Software Defect Characterization

    Takashi Watanabe, Akito Monden, Zeynep Yucel, Yasutaka Kamei, Shuji Morisaki

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E101D ( 9 )   2269 - 2278   2018.9

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    Association rule mining discovers relationships among variables in a data set, representing them as rules. These are expected to often have predictive abilities, that is, to be able to predict future events, but commonly used rule interestingness measures, such as support and confidence, do not directly assess their predictive power. This paper proposes a cross-validation -based metric that quantifies the predictive power of such rules for characterizing software defects. The results of evaluation this metric experimentally using four open-source data sets (Mylyn, NetBeans, Apache Ant and jEdit) show that it can improve rule prioritization performance over conventional metrics (support, confidence and odds ratio) by 72.8% for Mylyn, 15.0% for NetBeans, 10.5% for Apache Ant and 0 for jEdit in terms of SumNormPre(100) precision criterion. This suggests that the proposed metric can provide better rule prioritization performance than conventional metrics and can at least provide similar performance even in the worst case.

    DOI: 10.1587/transinf.2018EDP7020

  • Cross-Validation-Based Association Rule Prioritization Metric for Software Defect Characterization

    Takashi Watanabe, Akito Monden, Zeynep Yucel, Yasutaka Kamei, Shuji Morisaki

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E101D ( 9 )   2269 - 2278   2018.9

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    Association rule mining discovers relationships among variables in a data set, representing them as rules. These are expected to often have predictive abilities, that is, to be able to predict future events, but commonly used rule interestingness measures, such as support and confidence, do not directly assess their predictive power. This paper proposes a cross-validation -based metric that quantifies the predictive power of such rules for characterizing software defects. The results of evaluation this metric experimentally using four open-source data sets (Mylyn, NetBeans, Apache Ant and jEdit) show that it can improve rule prioritization performance over conventional metrics (support, confidence and odds ratio) by 72.8% for Mylyn, 15.0% for NetBeans, 10.5% for Apache Ant and 0 for jEdit in terms of SumNormPre(100) precision criterion. This suggests that the proposed metric can provide better rule prioritization performance than conventional metrics and can at least provide similar performance even in the worst case.

    DOI: 10.1587/transinf.2018EDP7020

  • Empirical study on the relationship between developer's working habits and efficiency

    Ariel Rodriguez, Fumiya Tanaka, Yasutaka Kamei

    15th ACM/IEEE International Conference on Mining Software Repositories, MSR 2018, co-located with the 40th International Conference on Software Engineering, ICSE 2018 Proceedings - 2018 ACM/IEEE 15th International Conference on Mining Software Repositories, MSR 2018   74 - 77   2018.5

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    Software developers can have a reputation for frequently working long and irregular hours which are widely considered to inhibit mental capacity and negatively affect work quality. This paper analyzes the working habits of software developers and the effects these habits have on efficiency based on a large amount of data extracted from the actions of developers in the IDE (Integrated Development Environment), Visual Studio. We use events that recorded the times at which all developer actions were performed along with the numbers of successful and failed build and test events. Due to the high level of detail of the events provided by KaVE project's tool, we were able to analyze the data in a way that previous studies have not been able to. We structure our study along three dimensions: (1) days of the week, (2) time of the day, and (3) continuous work. Our findings will help software developers and team leaders to appropriatly allocate working times and to maximize work quality.

    DOI: 10.1145/3196398.3196458

  • Poster: Exploring uncertainty in GitHub OSS projects: When and how do developers face uncertainty?

    Naoyasu Ubayashi, Hokuto Muraoka, Daiki Muramoto, Yasutaka Kamei, Ryosuke Sato

    Proceedings - International Conference on Software Engineering   272 - 273   2018.5

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    © 2018 Authors. Recently, many developers begin to notice that uncertainty is a crucial problem in software development. Unfortunately, no one knows how often uncertainty appears or what kinds of uncertainty exist in actual projects, because there are no empirical studies on uncertainty. To deal with this problem, we conduct a large-scale empirical study analyzing commit messages and revision histories of 1,444 OSS projects selected from the GitHub repositories.

    DOI: 10.1145/3183440.3194966

  • Empirical study on the relationship between developer's working habits and efficiency

    Ariel Rodriguez, Fumiya Tanaka, Yasutaka Kamei

    Proceedings - International Conference on Software Engineering   74 - 77   2018.5

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    © 2018 ACM. Software developers can have a reputation for frequently working long and irregular hours which are widely considered to inhibit mental capacity and negatively affect work quality. This paper analyzes the working habits of software developers and the effects these habits have on efficiency based on a large amount of data extracted from the actions of developers in the IDE (Integrated Development Environment), Visual Studio. We use events that recorded the times at which all developer actions were performed along with the numbers of successful and failed build and test events. Due to the high level of detail of the events provided by KaVE project's tool, we were able to analyze the data in a way that previous studies have not been able to. We structure our study along three dimensions: (1) days of the week, (2) time of the day, and (3) continuous work. Our findings will help software developers and team leaders to appropriatly allocate working times and to maximize work quality.

    DOI: 10.1145/3196398.3196458

  • Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction

    Shane McIntosh, Yasutaka Kamei

    IEEE Transactions on Software Engineering   44 ( 5 )   412 - 428   2018.5

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    © 2017 IEEE. Just-In-Time (JIT) models identify fix-inducing code changes. JIT models are trained using techniques that assume that past fix-inducing changes are similar to future ones. However, this assumption may not hold, e.g., as system complexity tends to accrue, expertise may become more important as systems age. In this paper, we study JIT models as systems evolve. Through a longitudinal case study of 37,524 changes from the rapidly evolving Qt and OpenStack systems, we find that fluctuations in the properties of fix-inducing changes can impact the performance and interpretation of JIT models. More specifically: (a) the discriminatory power (AUC) and calibration (Brier) scores of JIT models drop considerably one year after being trained; (b) the role that code change properties (e.g., Size, Experience) play within JIT models fluctuates over time; and (c) those fluctuations yield over- and underestimates of the future impact of code change properties on the likelihood of inducing fixes. To avoid erroneous or misleading predictions, JIT models should be retrained using recently recorded data (within three months). Moreover, quality improvement plans should be informed by JIT models that are trained using six months (or more) of historical data, since they are more resilient to period-specific fluctuations in the importance of code change properties.

    DOI: 10.1109/TSE.2017.2693980

  • Poster: Exploring uncertainty in GitHub OSS projects: When and how do developers face uncertainty?

    Naoyasu Ubayashi, Hokuto Muraoka, Daiki Muramoto, Yasutaka Kamei, Ryosuke Sato

    Proceedings - International Conference on Software Engineering   272 - 273   2018.5

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    © 2018 Authors. Recently, many developers begin to notice that uncertainty is a crucial problem in software development. Unfortunately, no one knows how often uncertainty appears or what kinds of uncertainty exist in actual projects, because there are no empirical studies on uncertainty. To deal with this problem, we conduct a large-scale empirical study analyzing commit messages and revision histories of 1,444 OSS projects selected from the GitHub repositories.

    DOI: 10.1145/3183440.3194966

  • OSS事前品質評価における重み付け手法の実証実験 Reviewed

    中野 大扉, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖, 高山 修一, 岩崎 孝司

    コンピュータソフトウェア   2018.3

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  • Stack Overflowを利用した自動バグ修正の検討 Reviewed

    廣瀬 賢幸, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    コンピュータソフトウェア   2018.3

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  • IArch-U: Interface-Centric Integrated Uncertainty-Aware Development Environment

    Keisuke Watanabe, Naoyasu Ubayashi, Takuya Fukamachi, Shunya Nakamura, Hokuto Muraoka, Yasutaka Kamei

    Proceedings - 2017 IEEE/ACM 9th International Workshop on Modelling in Software Engineering, MiSE 2017   40 - 46   2017.6

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    © 2017 IEEE. Uncertainty can appear in all aspects of software development: Uncertainty in requirements analysis, design decisions, implementation and testing. If uncertainty can be dealt with modularly, we can add or delete uncertain concerns to/from models, code and tests whenever these concerns arise or are fixed to certain concerns. To deal with this problem, we developed iArch-U, an IDE (Integrated Development Environment) for managing uncertainty modularly in all phases in software development. In this paper, we introduce an overview of iArch-U. The iArch-U IDE is open source software and can be downloaded from GitHub.

    DOI: 10.1109/MiSE.2017.7

  • The impact of using regression models to build defect classifiers

    Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan

    IEEE International Working Conference on Mining Software Repositories   135 - 145   2017.6

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    © 2017 IEEE. It is common practice to discretize continuous defect counts into defective and non-defective classes and use them as a target variable when building defect classifiers (discretized classifiers). However, this discretization of continuous defect counts leads to information loss that might affect the performance and interpretation of defect classifiers. Another possible approach to build defect classifiers is through the use of regression models then discretizing the predicted defect counts into defective and non-defective classes (regression-based classifiers). In this paper, we compare the performance and interpretation of defect classifiers that are built using both approaches (i.e., discretized classifiers and regression-based classifiers) across six commonly used machine learning classifiers (i.e., linear/logistic regression, random forest, KNN, SVM, CART, and neural networks) and 17 datasets. We find that: i) Random forest based classifiers outperform other classifiers (best AUC) for both classifier building approaches, ii) In contrast to common practice, building a defect classifier using discretized defect counts (i.e., discretized classifiers) does not always lead to better performance. Hence we suggest that future defect classification studies should consider building regression-based classifiers (in particular when the defective ratio of the modeled dataset is low). Moreover, we suggest that both approaches for building defect classifiers should be explored, so the best-performing classifier can be used when determining the most influential features.

    DOI: 10.1109/MSR.2017.4

  • iArch-U: Interface-Centric Integrated Uncertainty-aware Development Environment Reviewed International journal

    Keisuke Watanabe, Naoyasu Ubayashi, Takuya Fukamachi, Shunya Nakamura, Hokuto Muraoka, Yasutaka Kamei

    International Workshop on Modeling in Software Engineering (MiSE2017)   2017.5

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  • Automated A/B Testing with Declarative Variability Expressions

    Keisuke Watanabe, Takuya Fukamachi, Naoyasu Ubayashi, Yasutaka Kamei

    Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017   387 - 388   2017.4

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    © 2017 IEEE. A/B testing is the experiment strategy, which is often used on web or mobile application development. In A/B testing, a developer has to implement multiple variations of application, assign each variation to a subset of the entire user population randomly, and analyze log data to decide which variation should be used as a final product. Therefore, it is challenging to keep the application code clean in A/B testing, because defining variations of software or assigning user to each variation needs the modification of code. In fact there are some existing tools to approach this problem. Considering such a context of A/B testing research, we propose the solution based on the interface Archface-U and AOP (Aspect Oriented Programming) which aims to minimize the complication of code in A/B testing.

    DOI: 10.1109/ICSTW.2017.72

  • Erratum to: Studying high impact fix-inducing changes (Empirical Software Engineering, (2016), 21, 2, (605-641), 10.1007/s10664-015-9370-z)

    Ayse Tosun, Emad Shihab, Yasutaka Kamei

    Empirical Software Engineering   22 ( 2 )   848   2017.4

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    © 2016, Springer Science+Business Media New York The original version of this article unfortunately contained a mistake. The name of the third author was incorrectly displayed as BYasukata Kamei^. The correct information is as shown above.

    DOI: 10.1007/s10664-016-9455-3

  • Automated A/B Testing with Declarative Variability Expressions

    Keisuke Watanabe, Takuya Fukamachi, Naoyasu Ubayashi, Yasutaka Kamei

    Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017   387 - 388   2017.4

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    © 2017 IEEE. A/B testing is the experiment strategy, which is often used on web or mobile application development. In A/B testing, a developer has to implement multiple variations of application, assign each variation to a subset of the entire user population randomly, and analyze log data to decide which variation should be used as a final product. Therefore, it is challenging to keep the application code clean in A/B testing, because defining variations of software or assigning user to each variation needs the modification of code. In fact there are some existing tools to approach this problem. Considering such a context of A/B testing research, we propose the solution based on the interface Archface-U and AOP (Aspect Oriented Programming) which aims to minimize the complication of code in A/B testing.

    DOI: 10.1109/ICSTW.2017.72

  • Erratum to: Studying high impact fix-inducing changes (Empirical Software Engineering, (2016), 21, 2, (605-641), 10.1007/s10664-015-9370-z)

    Ayse Tosun, Emad Shihab, Yasutaka Kamei

    Empirical Software Engineering   22 ( 2 )   848   2017.4

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    © 2016, Springer Science+Business Media New York The original version of this article unfortunately contained a mistake. The name of the third author was incorrectly displayed as BYasukata Kamei^. The correct information is as shown above.

    DOI: 10.1007/s10664-016-9455-3

  • Industry Application of Software Development Task Measurement System: TaskPit

    Pawin Suthipornopas, Pattara Leelaprute, Akito Monden, Hidetake Uwano, Yasutaka Kamei, Naoyasu Ubayashi, Kenji Araki, Kingo Yamada, Ken Ichi Matsumoto

    IEICE Transactions on Information and Systems   E100D ( 3 )   462 - 472   2017.3

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    © 2017 The Institute of Electronics, Information and Communication Engineers. To identify problems in a software development process, we have been developing an automated measurement tool called TaskPit, which monitors software development tasks such as programming, testing and documentation based on the execution history of software applications. This paper introduces the system requirements, design and implementation of TaskPit; then, presents two real-world case studies applying TaskPit to actual software development. In the first case study, we applied TaskPit to 12 software developers in a certain software development division. As a result, several concerns (to be improved) have been revealed such as (a) a project leader spent too much time on development tasks while he was supposed to be a manager rather than a developer, (b) several developers rarely used e-mails despite the company's instruction to use e-mail as much as possible to leave communication records during development, and (c) several developers wrote too long e-mails to their customers. In the second case study, we have recorded the planned, actual, and self reported time of development tasks. As a result, we found that (d) there were unplanned tasks in more than half of days, and (e) the declared time became closer day by day to the actual time measured by TaskPit. These findings suggest that TaskPit is useful not only for a project manager who is responsible for process monitoring and improvement but also for a developer who wants to improve by him/herself.

    DOI: 10.1587/transinf.2016EDP7222

  • コードレビュー分析におけるデータクレンジングの影響調査 Reviewed

    戸田 航史, 亀井 靖高, 吉田 則裕,

    情報処理学会論文誌   2017.3

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  • Industry Application of Software Development Task Measurement System : TaskPit Reviewed

    Pawin Suthipornopas, Pattara Leelaprute, Akito Monden, Hidetake Uwano, Yasutaka Kamei, Naoyasu Ubayashi, Kenji Araki, Kingo Yamada, Ken-ichi Matsumoto

    IEICE Transactions on Information and Systems   2017.3

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  • 宣言的な可変性記述によるA/Bテストの自動化 Reviewed

    渡辺 啓介, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    コンピュータソフトウェア   2017.2

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  • Why are commits being reverted? A comparative study of industrial and open source projects

    Junji Shimagaki, Yasutaka Kamei, Shane McIntosh, David Pursehouse, Naoyasu Ubayashi

    Proceedings - 2016 IEEE International Conference on Software Maintenance and Evolution, ICSME 2016   301 - 311   2017.1

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    © 2016 IEEE. Software development is a cyclic process of integrating new features while introducing and fixing defects. During development, commits that modify source code files are uploaded to version control systems. Occasionally, these commits need to be reverted, i.e., the code changes need to be completely backed out of the software project. While one can often speculate about the purpose of reverted commits (e.g., the commit may have caused integration or build problems), little empirical evidence exists to substantiate such claims. The goal of this paper is to better understand why commits are reverted in large software systems. To that end, we quantitatively and qualitatively study two proprietary and four open source projects to measure: (1) the proportion of commits that are reverted, (2) the amount of time that commits that are eventually reverted linger within a codebase, and (3) the most frequent reasons why commits are reverted. Our results show that 1%-5% of the commits in the studied systems are reverted. Those commits that are eventually reverted linger within the studied codebases for 1-35 days (median). Furthermore, we identify 13 common reasons for reverting commits, and observe that the frequency of reverted commits of each reason varies broadly from project to project. A complementary qualitative analysis suggests that many reverted commits could have been avoided with better team communication and change awareness. Our findings made Sony Mobile's stakeholders aware that internally reverted commits can be reduced by paying more attention to their own changes. On the other hand, externally reverted commits could be minimized only if external stakeholders are involved to improve inter-company communication or requirements elicitation.

    DOI: 10.1109/ICSME.2016.83

  • Using Analytics to Quantify the Interest of Self-Admitted Technical Debt Reviewed International journal

    Yasutaka Kamei, Everton Maldonado, Emad Shihab, Naoyasu Ubayashi

    International Workshop on Technical Debt Analytics (TDA2016)   2016.12

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  • Why are Commits being Reverted? A Comparative Study of Industrial and Open Source Projects Reviewed International journal

    Junji Shimagaki, Yasutaka Kamei, Shane Mcintosh, David Pursehouse and Naoyasu Ubayashi

    International Conference on Software Maintenance and Evolution (ICSME2016)   2016.10

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    Software development is a cyclic process of integrating new features while introducing and fixing defects. During development, commits that modify source code files are uploaded to version control systems. Occasionally, these commits need to be reverted, i.e., the code changes need to be completely backed out of the software project. While one can often speculate about the purpose of reverted commits (e.g., the commit may have caused integration or build problems), little empirical evidence exists to substantiate such claims. The goal of this paper is to better understand why commits are reverted in large software systems. To that end, we quantitatively and qualitatively study two proprietary and four open source projects to measure: (1) the proportion of commits that are reverted, (2) the amount of time that commits that are eventually reverted linger within a codebase, and (3) the most frequent reasons why commits are reverted. Our results show that 1%-5% of the commits in the studied systems are reverted. Those commits that are eventually reverted linger within the studied codebases for 1-35 days (median). Furthermore, we identify 13 common reasons for reverting commits, and observe that the frequency of reverted commits of each reason varies broadly from project to project. A complementary qualitative analysis suggests that many reverted commits could have been avoided with better team communication and change awareness. Our findings made Sony Mobile’s stakeholders aware that internally reverted commits can be reduced by paying more attention to their own changes. On the other hand, externally reverted commits could be minimized only if external stakeholders are involved to improve inter-company communication or requirements elicitation.

  • Empirical Evaluation of Cross-Release Effort-Aware Defect Prediction Models

    Kwabena Ebo Bennin, Koji Toda, Yasutaka Kamei, Jacky Keung, Akito Monden, Naoyasu Ubayashi

    Proceedings - 2016 IEEE International Conference on Software Quality, Reliability and Security, QRS 2016   214 - 221   2016.10

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    © 2016 IEEE. To prioritize quality assurance efforts, various fault prediction models have been proposed. However, the best performing fault prediction model is unknown due to three major drawbacks: (1) comparison of few fault prediction models considering small number of data sets, (2) use of evaluation measures that ignore testing efforts and (3) use of n-fold cross-validation instead of the more practical cross-release validation. To address these concerns, we conducted cross-release evaluation of 11 fault density prediction models using data sets collected from 2 releases of 25 open source software projects with an effort-Aware performance measure known as Norm(Popt). Our result shows that, whilst M5 and K∗ had the best performances, they were greatly influenced by the percentage of faulty modules present and size of data set. Using Norm(Popt) produced an overall average performance of more than 50% across all the selected models clearly indicating the importance of considering testing efforts in building fault-prone prediction models.

    DOI: 10.1109/QRS.2016.33

  • An empirical study of the impact of modern code review practices on software quality

    Shane McIntosh, Yasutaka Kamei, Bram Adams, Ahmed E. Hassan

    Empirical Software Engineering   21 ( 5 )   2146 - 2189   2016.10

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    © 2015, Springer Science+Business Media New York. Software code review, i.e., the practice of having other team members critique changes to a software system, is a well-established best practice in both open source and proprietary software domains. Prior work has shown that formal code inspections tend to improve the quality of delivered software. However, the formal code inspection process mandates strict review criteria (e.g., in-person meetings and reviewer checklists) to ensure a base level of review quality, while the modern, lightweight code reviewing process does not. Although recent work explores the modern code review process, little is known about the relationship between modern code review practices and long-term software quality. Hence, in this paper, we study the relationship between post-release defects (a popular proxy for long-term software quality) and: (1) code review coverage, i.e., the proportion of changes that have been code reviewed, (2) code review participation, i.e., the degree of reviewer involvement in the code review process, and (3) code reviewer expertise, i.e., the level of domain-specific expertise of the code reviewers. Through a case study of the Qt, VTK, and ITK projects, we find that code review coverage, participation, and expertise share a significant link with software quality. Hence, our results empirically confirm the intuition that poorly-reviewed code has a negative impact on software quality in large systems using modern reviewing tools.

    DOI: 10.1007/s10664-015-9381-9

  • Predicting Crashing Releases of Mobile Applications Reviewed International journal

    Xin Xia, Emad Shihab, Yasutaka Kamei, David Lo and Xinyu Wang

    International Symposium on Empirical Software Engineering and Measurement (ESEM), (To appear). (Ciudad Real, Spain).   2016.9

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    Context: The quality of mobile applications has a vital impact on their user’s experience, ratings and ultimately overall success. Given the high competition in the mobile application market, i.e., many mobile applications perform the same or similar functionality, users of mobile apps tend to be less tolerant to quality issues.
    Goal: Therefore, identifying these crashing releases early on so that they can be avoided will help mobile app developers keep their user base and ensure the overall success of their apps.
    Method: To help mobile developers, we use machine learning techniques to effectively predict mobile app releases that are more likely to cause crashes, i.e., crashing releases. To perform our prediction, we mine and use a number of factors about the mobile releases, that are grouped into six unique dimensions: complexity, time, code, diffusion, commit, and text, and use a Naive Bayes classified to perform our prediction.
    Results: We perform an empirical study on 10 open source mobile applications containing a total of 2,638 releases from the F-Droid repository. On average, our approach can achieve F1 and AUC scores that improve over a baseline (random) predictor by 50% and 28%, respectively. We also find that factors related to text extracted from the commit logs prior to a release are the best predictors of crashing releases and have the largest effect.
    Conclusions: Ourproposedapproachcouldhelptoidentifycrash releases for mobile apps.

  • The Impact of Task Granularity on Co-evolution Analyses

    Keisuke Miura, Shane McIntosh, Yasutaka Kamei, Ahmed E. Hassan, Naoyasu Ubayashi

    International Symposium on Empirical Software Engineering and Measurement   08-09-September-2016   2016.9

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    © 2016 ACM. Background: Substantial research in the software evolution field aims to recover knowledge about development from the project history that is archived in repositories, such as a Version Control System (VCS). However, the data that is archived in these repositories can be analyzed at different levels of granularity. Although software evolution is a well-studied phenomenon at the revision-level, revisions may be too fine-grained to accurately represent development tasks. Aim: In this paper, we set out to understand the impact that the revision granularity has on co-change analyses. Method: We conduct an empirical study of 14 open source systems that are developed by the Apache Software Foundation. To understand the impact that the revision granularity may have on co-change activity, we study work items, i.e., logical groups of revisions that address a single issue. Results: We find that work item grouping has the potential to impact co-change activity, since 29% of work items consist of 2 or more revisions in 7 of the 14 studied systems. Deeper quantitative analysis shows that, in 7 of the 14 studied systems: (1) 11% of largest work items are entirely composed of small revisions, and would be missed by traditional approaches to filter or analyze large changes, (2) 83% of revisions that co-change under a single work item cannot be grouped using the typical configuration of the sliding time window technique and (3) 48% of work items that involve multiple developers cannot be grouped at the revision-level. Conclusions: Since the work item granularity is the natural means that practitioners use to separate development tasks, future software evolution studies, especially co-change analyses, should be conducted at the work item level.

    DOI: 10.1145/2961111.2962607

  • Predicting Crashing Releases of Mobile Applications

    Xin Xia, Emad Shihab, Yasutaka Kamei, David Lo, Xinyu Wang

    International Symposium on Empirical Software Engineering and Measurement   08-09-September-2016   2016.9

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    © 2016 ACM. Context: The quality of mobile applications has a vital impact on their user's experience, ratings and ultimately overall success. Given the high competition in the mobile application market, i.e., many mobile applications perform the same or similar functionality, users of mobile apps tend to be less tolerant to quality issues. Goal: Therefore, identifying these crashing releases early on so that they can be avoided will help mobile app developers keep their user base and ensure the overall success of their apps. Method: To help mobile developers, we use machine learning techniques to effectively predict mobile app releases that are more likely to cause crashes, i.e., crashing releases. To perform our prediction, we mine and use a number of factors about the mobile releases, that are grouped into six unique dimensions: complexity, time, code, diffusion, commit, and text, and use a Naive Bayes classified to perform our prediction. Results: We perform an empirical study on 10 open source mobile applications containing a total of 2,638 releases from the F-Droid repository. On average, our approach can achieve F1 and AUC scores that improve over a baseline (random) predictor by 50% and 28%, respectively. We also find that factors related to text extracted from the commit logs prior to a release are the best predictors of crashing releases and have the largest effect. Conclusions: Our proposed approach could help to identify crash releases for mobile apps.

    DOI: 10.1145/2961111.2962606

  • The Impact of Task Granularity on Co-evolution Analyses Reviewed International journal

    Keisuke Miura, Shane Mcintosh, Yasutaka Kamei, Ahmed E. Hassan and Naoyasu Ubayashi

    International Symposium on Empirical Software Engineering and Measurement (ESEM), (To appear). (Ciudad Real, Spain).   2016.9

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    Aim: In this paper, we set out to understand the impact that the revision granularity has on co-change analyses. Method: We conduct an empirical study of 14 open source systems that are developed by the Apache Software Foundation. To understand the impact that the revision granularity may have on co-change activity, we study work items, i.e., logical groups of revisions that address a single issue. Results: We find that work item grouping has the poten- tial to impact co-change activity, since 29% of work items consist of 2 or more revisions in 7 of the 14 studied systems. Deeper quantitative analysis shows that, in 7 of the 14 studied systems: (1) 11% of largest work items are entirely composed of small revisions, and would be missed by traditional approaches to filter or analyze large changes, (2) 83% of revisions that co-change under a single work item cannot be grouped using the typical configuration of the sliding time window technique and (3) 48% of work items that involve multiple developers cannot be grouped at the revision-level. Conclusions: Since the work item granularity is the natural means that practitioners use to separate development tasks, future software evolution studies, especially co-change analyses, should be conducted at the work item level.

  • Empirical Evaluation of Cross-Release Effort-Aware Defect Prediction Models Reviewed International journal

    Kwabena Ebo Bennin, Koji Toda, Yasutaka Kamei, Jacky Keung, Akito Monden and Naoyasu Ubayashi

    International Conference on Software Quality, Reliability and Security (QRS2016)   2016.8

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  • Investigating the Effects of Balanced Training and Testing Datasets on Effort-Aware Fault Prediction Models

    Kwabena Ebo Bennin, Jacky Keung, Akito Monden, Yasutaka Kamei, Naoyasu Ubayashi

    Proceedings - International Computer Software and Applications Conference   1   154 - 163   2016.8

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    © 2016 IEEE. To prioritize software quality assurance efforts, faultprediction models have been proposed to distinguish faulty modules from clean modules. The performances of such models are often biased due to the skewness or class imbalance of the datasets considered. To improve the prediction performance of these models, sampling techniques have been employed to rebalance the distribution of fault-prone and non-fault-prone modules. The effect of these techniques have been evaluated in terms of accuracy/geometric mean/F1-measure in previous studies, however, these measures do not consider the effort needed to fixfaults. To empirically investigate the effect of sampling techniqueson the performance of software fault prediction models in a morerealistic setting, this study employs Norm(Popt), an effort-awaremeasure that considers the testing effort. We performed two setsof experiments aimed at (1) assessing the effects of samplingtechniques on effort-aware models and finding the appropriateclass distribution for training datasets (2) investigating the roleof balanced training and testing datasets on performance ofpredictive models. Of the four sampling techniques applied, the over-sampling techniques outperformed the under-samplingtechniques with Random Over-sampling performing best withrespect to the Norm (Popt) evaluation measure. Also, performanceof all the prediction models improved when sampling techniqueswere applied between the rates of (20-30)% on the trainingdatasets implying that a strictly balanced dataset (50% faultymodules and 50% clean modules) does not result in the bestperformance for effort-aware models. Our results also indicatethat performances of effort-aware models are significantly dependenton the proportions of the two types of the classes in thetesting dataset. Models trained on moderately balanced datasetsare more likely to withstand fluctuations in performance as theclass distribution in the testing data varies.

    DOI: 10.1109/COMPSAC.2016.144

  • Identifying recurring association rules in software defect prediction

    Takashi Watanabe, Akito Monden, Yasutaka Kamei, Shuji Morisaki

    2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings   861 - 866   2016.8

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    © 2016 IEEE. Association rule mining discovers patterns of co-occurrences of attributes as association rules in a data set. The derived association rules are expected to be recurrent, that is, the patterns recur in future in other data sets. This paper defines the recurrence of a rule, and aims to find a criteria to distinguish between high recurrent rules and low recurrent ones using a data set for software defect prediction. An experiment with the Eclipse Mylyn defect data set showed that rules of lower than 30 transactions showed low recurrence. We also found that the lower bound of transactions to select high recurrence rules is dependent on the required precision of defect prediction.

    DOI: 10.1109/ICIS.2016.7550867

  • Thresholds for Size and Complexity Metrics: A Case Study from the Perspective of Defect Density Reviewed International journal

    Kazuhiro Yamashita, Changyun Huang, Meiyappan Nagappan, Yasutaka Kamei, Audris Mockus, Ahmed E. Hassan and Naoyasu Ubayashi

    International Conference on Software Quality, Reliability and Security (QRS2016)   2016.8

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  • Identifying Recurring Association Rules in Software Defect Prediction Reviewed International journal

    Takashi Watanabe, Akito Monden, Yasutaka Kamei, Shuji Morisaki

    International Conference on Computer and Information Science (ICIS2016)   2016.6

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  • Investigating the Effects of Balanced Training and Testing Data Sets on Effort-Aware Fault Prediction Models Reviewed International journal

    Kwabena Ebo Bennin, Jacky Keung, Akito Monden, Yasutaka Kamei and Naoyasu Ubayashi

    International Conference on Computers, Software and Applications (COMPSAC)   2016.6

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  • Assessing the differences of clone detection methods used in the fault-prone module prediction

    Masateru Tsunoda, Yasutaka Kamei, Atsushi Sawada

    2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016   15 - 16   2016.5

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    © 2016 IEEE. We have investigated through several experiments the differences in the fault-prone module prediction accuracy caused by the differences in the constituent code clone metrics of the prediction model. In the previous studies, they use one or more code clone metrics as independent variables to build an accurate prediction model. While they often use the clone detection method proposed by Kamiya et al. to calculate these metrics, the effect of the detection method on the prediction accuracy is not clear. In the experiment, we built prediction models using a dataset collected from an open source software project. The result suggests that the prediction accuracy is improved, when clone metrics derived from the various clone detection tool are used.

    DOI: 10.1109/SANER.2016.65

  • Assessing the differences of clone detection methods used in the fault-prone module prediction

    Masateru Tsunoda, Yasutaka Kamei, Atsushi Sawada

    2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016   15 - 16   2016.5

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    © 2016 IEEE. We have investigated through several experiments the differences in the fault-prone module prediction accuracy caused by the differences in the constituent code clone metrics of the prediction model. In the previous studies, they use one or more code clone metrics as independent variables to build an accurate prediction model. While they often use the clone detection method proposed by Kamiya et al. to calculate these metrics, the effect of the detection method on the prediction accuracy is not clear. In the experiment, we built prediction models using a dataset collected from an open source software project. The result suggests that the prediction accuracy is improved, when clone metrics derived from the various clone detection tool are used.

    DOI: 10.1109/SANER.2016.65

  • A Review and Comparison of Methods for Determining the Best Analogies in Analogy-based Software Effort Estimation Reviewed International journal

    Bodin Chinthanet, Passakorn Phannachitta, Yasutaka Kamei, Pattara Leelaprute, Arnon Rungsawang, Naoyasu Ubayashi and Kenichi Matsumoto

    International Symposium on Applied Computing (SAC 2016) Poster Session   2016.4

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  • Studying high impact fix-inducing changes

    Ayse Tosun Misirli, Emad Shihab, Yasukata Kamei

    Empirical Software Engineering   21 ( 2 )   605 - 641   2016.4

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    © 2015, Springer Science+Business Media New York. As software systems continue to play an important role in our daily lives, their quality is of paramount importance. Therefore, a plethora of prior research has focused on predicting components of software that are defect-prone. One aspect of this research focuses on predicting software changes that are fix-inducing. Although the prior research on fix-inducing changes has many advantages in terms of highly accurate results, it has one main drawback: It gives the same level of impact to all fix-inducing changes. We argue that treating all fix-inducing changes the same is not ideal, since a small typo in a change is easier to address by a developer than a thread synchronization issue. Therefore, in this paper, we study high impact fix-inducing changes (HIFCs). Since the impact of a change can be measured in different ways, we first propose a measure of impact of the fix-inducing changes, which takes into account the implementation work that needs to be done by developers in later (fixing) changes. Our measure of impact for a fix-inducing change uses the amount of churn, the number of files and the number of subsystems modified by developers during an associated fix of the fix-inducing change. We perform our study using six large open source projects to build specialized models that identify HIFCs, determine the best indicators of HIFCs and examine the benefits of prioritizing HIFCs. Using change factors, we are able to predict 56 % to 77 % of HIFCs with an average false alarm (misclassification) rate of 16 %. We find that the lines of code added, the number of developers who worked on a change, and the number of prior modifications on the files modified during a change are the best indicators of HIFCs. Lastly, we observe that a specialized model for HIFCs can provide inspection effort savings of 4 % over the state-of-the-art models. We believe our results would help practitioners prioritize their efforts towards the most impactful fix-inducing changes and save inspection effort.

    DOI: 10.1007/s10664-015-9370-z

  • A review and comparison of methods for determining the best analogies in analogy-based software effort estimation

    Bodin Chinthanet, Pattara Leelaprute, Arnon Rungsawang, Passakorn Phannachitta, Naoyasu Ubayashi, Yasutaka Kamei, Kenichi Matsumoto

    Proceedings of the ACM Symposium on Applied Computing   04-08-April-2016   1554 - 1557   2016.4

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    © 2016 ACM. Analogy-based effort estimation (ABE) is a commonly used software development effort estimation method. The processes of ABE are based on a reuse of effort values from similar past projects, where the appropriate numbers of past projects (k values) to be reused is one of the long-standing debates in ABE research studies. To date, many approaches to find this k value have been continually proposed. One important reason for this inconclusive debate is that different studies appear to produce different conclusions of the k value to be appropriate. Therefore, in this study, we revisit 8 common approaches to the k value being most appropriate in general situations. With a more robust and comprehensive evaluation methodology using 5 robust error measures subject to the Wilcoxon rank-sum statistical test, we found that conflicting results in the previous studies were not mainly due to the use of different methodologies nor different datasets, but the performance of the different approaches are actually varied widely.

    DOI: 10.1145/2851613.2851974

  • Software Quality Assurance 2.0: Proactive, Practical, and Relevant

    Yasutaka Kamei

    IEEE SOFTWARE   33 ( 2 )   102 - 103   2016.3

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  • Magnet or sticky? Measuring project characteristics from the perspective of developer attraction and retention

    Kazuhiro Yamashita, Yasutaka Kamei, Shane McIntosh, Ahmed E. Hassan, Naoyasu Ubayashi

    Journal of Information Processing   24 ( 2 )   339 - 348   2016.3

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    © 2016 Information Processing Society of Japan. Open Source Software (OSS) is vital to both end users and enterprises. As OSS systems are becoming a type of infrastructure, long-term OSS projects are desired. For the survival of OSS projects, the projects need to not only retain existing developers, but also attract new developers to grow. To better understand how projects retain and attract contributors, our preliminary study aimed to measure the personnel attraction and retention of OSS projects using a pair of population migration metrics, called Magnet (personnel attraction) and Sticky (retention) metrics. Because the preliminary study analyzed only 90 projects and the 90 projects are not representative of GitHub, this paper extend the preliminary study to better understand the generalizability of the results by analyzing 16,552 projects of GitHub. Furthermore, we also add a pilot study to investigate the typical duration between releases to find more appropriate release duration. The study results show that (1) approximately 23% of developers remain in the same projects that the developers contribute to, (2) the larger projects are likely to attract and retain more developers, (3) 53% of terminal projects eventually decay to a state of fewer than ten developers and (4) 55% of attractive projects remain in an attractive category.

    DOI: 10.2197/ipsjjip.24.339

  • Leaders of tomorrow on the future of software engineering: A roundtable

    Felienne Hermans, Janet Siegmund, Thomas Fritz, Gabriele Bavota, Meiyappan Nagappan, Abram Hindle, Yasutaka Kamei, Ali Mesbah, Bram Adams

    IEEE Software   33 ( 2 )   99 - 104   2016.3

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    © 1984-2012 IEEE. Nine rising stars in software engineering describe how software engineering research will evolve, highlighting emerging opportunities and groundbreaking solutions. They predict the rise of end-user programming, the monitoring of developers through neuroimaging and biometrics sensors, analysis of data from unstructured documents, the mining of mobile marketplaces, and changes to how we create and release software.

    DOI: 10.1109/MS.2016.55

  • Software Quality Assurance 2.0: Proactive, Practical, and Relevant

    Yasutaka Kamei

    IEEE SOFTWARE   33 ( 2 )   102 - 103   2016.3

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  • Magnet or Sticky? Measuring Project Characteristics from the Perspective of Developer Attraction and Retention Reviewed

    Kazuhiro Yamashita, Yasutaka Kamei, Shane McIntosh, Ahmed E. Hassan and Naoyasu Ubayashi

    Journal of Information Processing   2016.3

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  • Leaders of tomorrow on the future of software engineering: A roundtable

    Felienne Hermans, Janet Siegmund, Thomas Fritz, Gabriele Bavota, Meiyappan Nagappan, Abram Hindle, Yasutaka Kamei, Ali Mesbah, Bram Adams

    IEEE Software   33 ( 2 )   99 - 104   2016.3

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    © 1984-2012 IEEE. Nine rising stars in software engineering describe how software engineering research will evolve, highlighting emerging opportunities and groundbreaking solutions. They predict the rise of end-user programming, the monitoring of developers through neuroimaging and biometrics sensors, analysis of data from unstructured documents, the mining of mobile marketplaces, and changes to how we create and release software.

    DOI: 10.1109/MS.2016.55

  • 脳活動に基づくプログラム理解の困難さ測定 Reviewed

    中川 尊雄, 亀井 靖高, 上野 秀剛, 門田 暁人, 鵜林 尚靖, 松本 健一

    コンピュータソフトウェア   2015.11

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  • An Empirical Study of goto in C Code from GitHub Repositories Reviewed International journal

    Meiyappan Nagappan, Romain Robbes, Yasutaka Kamei, Eric Tanter, Shane Mcintosh, Audris Mockus, Ahmed E. Hassan

    the ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE2015)   2015.9

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  • Revisiting the Applicability of the Pareto Principle to Core Development Teams in Open Source Software Projects Reviewed International journal

    Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei, Ahmed E. Hassan and Naoyasu Ubayashi

    International Workshop on Principles of Software Evolution (IWPSE 2015)   2015.8

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  • Revisiting the applicability of the pareto principle to core development teams in open source software projects

    Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei, Ahmed E. Hassan, Naoyasu Ubayashi

    International Workshop on Principles of Software Evolution (IWPSE)   30-Aug-2015   46 - 55   2015.8

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    © 2015 ACM. It is often observed that the majority of the development work of an Open Source Software (OSS) project is contributed by a core team, i.e., a small subset of the pool of active developers. In fact, recent work has found that core development teams follow the Pareto principle-roughly 80% of the code contributions are produced by 20% of the active developers. However, those findings are based on samples of between one and nine studied systems. In this paper, we revisit prior studies about core developers using 2,496 projects hosted on GitHub. We find that even when we vary the heuristic for detecting core developers, and when we control for system size, team size, and project age: (1) the Pareto principle does not seem to apply for 40%-87% of GitHub projects; and (2) more than 88% of GitHub projects have fewer than 16 core developers. Moreover, we find that when we control for the quantity of contributions, bug fixing accounts for a similar proportion of the contributions of both core (18%-20%) and non-core developers (21%-22%). Our findings suggest that the Pareto principle is not compatible with the core teams of many GitHub projects. In fact, several of the studied GitHub projects are susceptible to the bus factor, where the impact of a core developer leaving would be quite harmful.

    DOI: 10.1145/2804360.2804366

  • Poster: Conquering Uncertainty in Java Programming

    Takuya Fukamachi, Naoyasu Ubayashi, Shintaro Hosoai, Yasutaka Kamei

    Proceedings - International Conference on Software Engineering   2   823 - 824   2015.8

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    © 2015 IEEE. Uncertainty in programming is one of the challenging issues to be tackled, because it is error-prone for many programmers to temporally avoid uncertain concerns only using simple language constructs such as comments and conditional statements. This paper proposes ucJava, a new Java programming environment for conquering uncertainty. Our environment provides a modular programming style for uncertainty and supports test-driven development taking uncertainty into consideration.

    DOI: 10.1109/ICSE.2015.266

  • An empirical study of goto in C code from github repositories

    Meiyappan Nagappan, Romain Robbes, Yasutaka Kamei, Éric Tanter, Shane Mcintosh, Audris Mockus, Ahmed E. Hassan

    2015 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2015 - Proceedings   404 - 414   2015.8

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    © 2015 ACM. It is nearly 50 years since Dijkstra argued that goto obscures the ow of control in program execution and urged programmers to abandon the goto statement. While past research has shown that goto is still in use, little is known about whether goto is used in the unrestricted manner that Dijkstra feared, and if it is harmful' enough to be a part of a post-release bug. We, therefore, conduct a two part empirical study - (1) qualitatively analyze a statistically representative sample of 384 files from a population of almost 250K C programming language files collected from over 11K GitHub repositories and find that developers use goto in C files for error handling (80:21 ± 5%) and cleaning up resources at the end of a procedure (40:36 ± 5%); and (2) quantitatively analyze the commit history from the release branches of six OSS projects and find that no goto statement was re- moved/modified in the post-release phase of four of the six projects. We conclude that developers limit themselves to using goto appropriately in most cases, and not in an un- restricted manner like Dijkstra feared, thus suggesting that goto does not appear to be harmful in practice.

    DOI: 10.1145/2786805.2786834

  • Modularity for Uncertainty Reviewed International journal

    Takuya Fukamachi, Naoyasu Ubayashi, Shintaro Hosoai, Yasutaka Kamei

    International Workshop on Modeling in Software Engineering (MiSE2015)   2015.5

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  • Studying High Impact Fix-Inducing Changes Reviewed International journal

    Ayse Tosun Misirli, Emad Shihab, Yasutaka Kamei

    Journal of Empirical Software Engineering   2015.5

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  • Poster: Conquering Uncertainty in Java Programming Reviewed International journal

    Takuya Fukamachi, Naoyasu Ubayashi, Shintaro Hosoai, Yasutaka Kamei

    International Conference on Software Engineering (ICSE2015), Poster Session.   2015.5

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  • Automated DSL Construction Based on Software Product Lines Reviewed International journal

    Changyun Huang, Ataru Osaka, Yasutaka Kamei, Naoyasu Ubayashi

    International Conference on Model-Driven Engineering and Software Development (MODELSWARD2015), Poster Session   2015.2

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  • 大規模OSS開発における不具合修正時間の短縮化を目的としたバグトリアージ手法

    柏 祐太郎, 大平 雅雄, 阿萬 裕久, 亀井 靖高

    情報処理学会論文誌   56 ( 2 )   669 - 681   2015.2

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    A Bug Triaging Method for Reducing the Time to Fix Bugs in Large-scale Open Source Software Development
    This paper proposes a bug triaging method to reduce the time to fix bugs in large-scale open source software development. Our method considers the upper limit of tasks which can be fixed by a developer in a certain period. In this paper, we conduct a case study of applying our method to Mozilla Firefox and Eclipse Platform projects and show the following findings: (1) using our method mitigates the situation where the majority of bug-fixing tasks are assigned to particular developers, (2) our method can reduce up to 50%-83% of time to fix bugs compared with the manual bug triaging method and up to 34%-38% compared with the existing method, and (3) the two factors, Preference (adequate for fixing a bug) and Limit (limits of developers' working hours), used in our method have an dispersion effect on the task assignment.

  • 大規模OSS開発における不具合修正時間の短縮化を目的としたバグトリアージ手法 Reviewed

    柏 祐太郎, 大平 雅雄, 阿萬 裕久, 亀井 靖高

    情報処理学会論文誌   2015.2

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  • Chromiumプロジェクトにおけるレビュー・パッチ開発経験がレビューに要する時間に与える影響の分析 Reviewed

    戸田 航史, 亀井 靖高, 濵﨑 一樹, 吉田 則裕

    コンピュータソフトウェア   2015.2

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  • Sketch-Based Gradual Model-Driven Development Reviewed International journal

    Peiyuan Li, Naoyasu Ubayashi, Di Ai, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei

    International Workshop on Innovative Software Development Methodologies and Practices (InnoSWDev 2014)   2014.11

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  • Uncertainty-aware architectural interface

    Naoyasu Ubayashi, Di Ai, Peiyuan Li, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei

    9th International Workshop on Advanced Modularization Techniques, AOAsia 2014 - Proceedings   4 - 6   2014.11

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    Copyright 2014 ACM. In most software development projects, design models tend to contain uncertainty, because all of the design concerns cannot be captured at the early development phase. It is preferable to be able to check consistency or traceability among design models and programs even if they contain uncertain concerns. To deal with this problem, we propose the notion of uncertainty-aware Archface, an interface mechanism exposing a set of architectural points that should be shared between design and code. We can explicitly describe uncertainty in design models or programs by specifying uncertain architectural points.

    DOI: 10.1145/2666358.2666579

  • Sketch-Based gradual model-driven development

    Peiyuan Li, Naoyasu Ubayashi, Di Ai, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei

    International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings   100 - 105   2014.11

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    Copyright © 2014 ACM. This paper proposes an abstraction-aware reverse engineering method in which a developer just makes a mark on an important code region as if he or she draws a quick sketch on the program list. A support tool called iArch slices a program from marked program points and generates an abstract design model faithful to the intention of the developer. The developer can modify the design model and re-generate the code again while preserving the abstraction level and the traceability. Archface, an interface mechanism between design and code, plays an important role in abstraction-aware traceability check. If the developer wants to obtain a more concrete design model from the code, he or she only has to make additional marks on the program list. We can gradually transition to model-driven development style.

    DOI: 10.1145/2666581.2666595

  • Early identification of future committers in open source software projects

    Akinori Ihara, Yasutaka Kamei, Masao Ohira, Ahmed E. Hassan, Naoyasu Ubayashi, Ken Ichi Matsumoto

    Proceedings - International Conference on Quality Software   47 - 56   2014.11

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    © 2014 IEEE. There exists two types of developers in Open Source Software (OSS) projects: 1) Committers who have permission to commit edited source code to the Version Control System (VCS), 2) Developers who contribute source code but cannot commit to the VCS directly. In order to develop and evolve high quality OSS, projects are always in search of new committers. OSS projects often promote strong developers to become committers. When existing committers find strong developers, they propose their promotion to a committer role. Delaying the committer-promotion might lead to strong developers departing from an OSS project and the project losing them. However early committer-promotion comes with its own slew of risks as well (e.g., the promotion of inexperienced developers). Hence, committer-promotion decisions are critical for the quality and successful evolution of OSS projects. In this paper, we examine the committer-promotion phenomena for two OSS projects (Eclipse and Firefox). We find that the amount of activities by future committers was higher than the amount of activities by developers who did not become committers). We also find that some developers are promoted to a committer role very rapidly (within a few month) while some of developers take over one year to become a committer. Finally, we develop a committer-identification model to assist OSS projects identifying future committers.

    DOI: 10.1109/QSIC.2014.30

  • Uncertainty-Aware Architectural Interface Reviewed International journal

    Naoyasu Ubayashi, Di Ai, Peiyuan Li, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei

    International Workshop on Advanced Modularization Techniques (AOAsia/Pacific 2014)   2014.11

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  • Early Identification of Future Committers in Open Source Software Projects Reviewed International journal

    Akinori Ihara, Yasutaka Kamei, Masao Ohira, Ahmed E. Hassan, Naoyasu Ubayashi and Kenichi Matsumoto

    International Conference on Quality Software (QSIC2014)   2014.10

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  • Abstraction-aware Verifying Compiler for Yet Another MDD Reviewed International journal

    Naoyasu Ubayashi, Di Ai, Peiyuan Li, Yu Ning Li, Shintaro Hosoai and Yasutaka Kamei

    International Conference on Automated Software Engineering (ASE 2014) [new ideas paper track]   2014.9

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  • プログラム理解の困難さの脳血流による計測の試み

    中川 尊雄, 亀井 靖高, 上野 秀剛, 門田 暁人, 松本 健一

    コンピュータ ソフトウェア   31 ( 3 )   3_270 - 3_276   2014.9

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    On Measuring the Difficulty of Program Comprehension based on Cerebral Blood Flow
    In this research, we aim to quantify the difficulty of program comprehension during source code reading. We use Near Infra-Red Spectroscopy(NIRS) to measure the activation of brain. As a result of an experiment with 10 subjects, 8 of them showed a strong activation in the brain during reading of strongly obfuscated programs that are extremely difficult to comprehend. We also normalized the data for each participant and aggregated them for statistical testing. As a result of t-test, significant difference (p < 0.001) was seen in the mean of the brain blood flow between obfuscated and non-obfuscated programs.

    DOI: 10.11309/jssst.31.3_270

  • プログラム理解の困難さの脳血流による計測の試み Reviewed

    中川 尊雄, 亀井 靖高, 上野 秀剛, 門田 暁人, 松本 健一

    コンピュータソフトウェア   2014.8

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  • A Case Study on Introducing the Design Thinking into PBL Reviewed International journal

    Shuhei Ohsako, Yasutaka Kamei, Shintaro Hosoai, Weiqiang Kong, Kimitaka Kato, Akihiko Ishizuka, Kazutoshi Sakaguchi, Miyuki Kawataka, Yoshitsugu Morita, Naoyasu Ubayashi and Akira Fukuda

    International Conference on Frontiers in Education: CS and CE (FECS 2014)   2014.7

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  • Magnet or Sticky?: An OSS Project-by-Project Typology Reviewed International journal

    Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei and Naoyasu Ubayashi

    International Working Conference on Mining Software Repositories (MSR 2014)   2014.6

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  • Quantifying Programmers' Mental Workload during Program Comprehension Based on Cerebral Blood Flow Measurement: A Controlled Experiment Reviewed International journal

    Takao Nakagawa, Yasutaka Kamei, Hidetake Uwano, Akito Monden, Kenichi Matsumoto and Daniel M. German

    International Conference on Software Engineering (ICSE2014), NIER Track   2014.6

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  • Simulation of effort allocation strategies in software testing using bug module

    Daisuke Nakano, Akito Monden, Yasutaka Kamei, Kenichi Matsumoto

    Computer Software   31 ( 2 )   118 - 128   2014.5

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    To date, various techniques for predicting fault-prone modules have been proposed; however, test strategies, which assign a certain amount of test effort to each module, have been rarely studied. This paper proposes a simulation model of software testing that can evaluate various test strategies. The simulation model estimates the number of discoverable faults with respect to the given test resources, the test strategy, complexity metrics of a set of modules to be tested, and the fault prediction results. Based on a case study of simulation applying fault prediction to two open source software (Eclipse and Mylyn), we show the relationship between the available test effort and the effective test strategy.

  • The impact of code review coverage and code review participation on Software quality: A case study of the Qt, VTK, and ITK projects

    Shane McIntosh, Yasutaka Kamei, Bram Adams, Ahmed E. Hassan

    11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings   192 - 201   2014.5

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    Copyright 2014 ACM. Software code review, i.e., the practice of having third-party team members critique changes to a software system, is a well-established best practice in both open source and proprietary software domains. Prior work has shown that the formal code inspections of the past tend to improve the quality of software delivered by students and small teams. However, the formal code inspection process mandates strict review criteria (e.g., in-person meetings and reviewer checklists) to ensure a base level of review quality, while the modern, lightweight code reviewing process does not. Although recent work explores the modern code review process qualitatively, little research quantitatively explores the relationship between properties of the modern code review process and software quality. Hence, in this paper, we study the relationship between software quality and: (1) code review coverage, i.e., the proportion of changes that have been code reviewed, and (2) code review participation, i.e., the degree of reviewer involvement in the code review process. Through a case study of the Qt, VTK, and ITK projects, we find that both code review coverage and participation share a significant link with software quality. Low code review coverage and participation are estimated to produce components with up to two and five additional post-release defects respectively. Our results empirically confirm the intuition that poorly reviewed code has a negative impact on software quality in large systems using modern reviewing tools.

    DOI: 10.1145/2597073.2597076

  • Simulation of effort allocation strategies in software testing using bug module

    Daisuke Nakano, Akito Monden, Yasutaka Kamei, Kenichi Matsumoto

    Computer Software   31 ( 2 )   118 - 128   2014.5

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    To date, various techniques for predicting fault-prone modules have been proposed; however, test strategies, which assign a certain amount of test effort to each module, have been rarely studied. This paper proposes a simulation model of software testing that can evaluate various test strategies. The simulation model estimates the number of discoverable faults with respect to the given test resources, the test strategy, complexity metrics of a set of modules to be tested, and the fault prediction results. Based on a case study of simulation applying fault prediction to two open source software (Eclipse and Mylyn), we show the relationship between the available test effort and the effective test strategy.

  • Magnet or sticky? An OSS project-by-project typology

    Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei, Naoyasu Ubayashi

    11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings   344 - 347   2014.5

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    Copyright 2014 ACM. For Open Source Software (OSS) projects, retaining existing contributors and attracting new ones is a major concern. In this paper, we expand and adapt a pair of population migration metrics to analyze migration trends in a collection of open source projects. Namely, we study: (1) project stickiness, i.e., its tendency to retain existing contributors and (2) project magnetism, i.e., its tendency to attract new contributors. Using quadrant plots, we classify projects as attractive (highly magnetic and sticky), stagnant (highly sticky, weakly magnetic), fluctuating (highly magnetic, weakly sticky), or terminal (weakly magnetic and sticky). Through analysis of the MSR challenge dataset, we find that: (1) quadrant plots can effectively identify at-risk projects, (2) stickiness is often motivated by professional activity and (3) transitions among quadrants as a project ages often coincides with interesting events in the evolution history of a project.

    DOI: 10.1145/2597073.2597116

  • An empirical study of just-in-time defect prediction using cross-project models

    Takafumi Fukushima, Yasutaka Kamei, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi

    11th Working Conference on Mining Software Repositories, MSR 2014 - Proceedings   172 - 181   2014.5

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    Copyright 2014 ACM. Prior research suggests that predicting defect-inducing changes, i.e., Just-In-Time (JIT) defect prediction is a more practical alternative to traditional defect prediction techniques, providing immediate feedback while design decisions are still fresh in the minds of developers. Unfortunately, similar to traditional defect prediction models, JIT models require a large amount of training data, which is not available when projects are in initial development phases. To address this flaw in traditional defect prediction, prior work has proposed cross-project models, i.e., models learned from older projects with sufficient history. However, cross-project models have not yet been explored in the context of JIT prediction. Therefore, in this study, we empirically evaluate the performance of JIT cross-project models. Through a case study on 11 open source projects, we find that in a JIT cross-project context: (1) high performance within-project models rarely perform well; (2) models trained on projects that have similar correlations between predictor and dependent variables often perform well; and (3) ensemble learning techniques that leverage historical data from several other projects (e.g., voting experts) often perform well. Our findings empirically confirm that JIT cross-project models learned using other projects are a viable solution for projects with little historical data. However, JIT cross-project models perform best when the data used to learn them is carefully selected.

    DOI: 10.1145/2597073.2597075

  • 上流工程での活動実績を用いたソフトウェア開発工数見積もり方法の定量的評価 Reviewed

    角田 雅照, 戸田 航史, 伏田 享平, 亀井 靖高, Meiyappan Nagappan, 鵜林 尚靖

    コンピュータソフトウェア   2014.5

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  • バグモジュール予測を用いたテスト工数割り当て戦略のシミュレーション Reviewed

    中野 大輔, 門田 暁人, 亀井 靖高, 松本 健一

    コンピュータソフトウェア   2014.5

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  • iArch: An IDE for Supporting Fluid Abstraction Reviewed International journal

    Di Ai, Naoyasu Ubayashi, Peiyuan Li, Daisuke Yamamoto, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei

    International Conference on Modularity'14, Tool Demo Session   2014.4

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  • Towards Language-Oriented Software Development Reviewed International journal

    Changyun Huang, Naoyasu Ubayashi and Yasutaka Kamei

    International Workshop on Open and Original Problems in Software Language Engineering (OOPSLE 2014)   2014.2

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  • iArch - An IDE for Supporting Abstraction-aware Design Traceability Reviewed International journal

    Di Ai, Naoyasu Ubayashi, Peiyuan Li, Shintaro Hosoai and Yasutaka Kamei

    International Conference on Model-Driven Engineering and Software Development (MODELSWARD2014), Poster Session   2014.1

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  • Is lines of code a good measure of effort in effort-aware models?

    Emad Shihab, Yasutaka Kamei, Bram Adams, Ahmed E. Hassan

    Information and Software Technology   55 ( 11 )   1981 - 1993   2013.11

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    Context Effort-aware models, e.g., effort-aware bug prediction models aim to help practitioners identify and prioritize buggy software locations according to the effort involved with fixing the bugs. Since the effort of current bugs is not yet known and the effort of past bugs is typically not explicitly recorded, effort-aware bug prediction models are forced to use approximations, such as the number of lines of code (LOC) of the predicted files. Objective Although the choice of these approximations is critical for the performance of the prediction models, there is no empirical evidence on whether LOC is actually a good approximation. Therefore, in this paper, we investigate the question: is LOC a good measure of effort for use in effort-aware models? Method We perform an empirical study on four open source projects, for which we obtain explicitly-recorded effort data, and compare the use of LOC to various complexity, size and churn metrics as measures of effort. Results We find that using a combination of complexity, size and churn metrics are a better measure of effort than using LOC alone. Furthermore, we examine the impact of our findings on previous effort-aware bug prediction work and find that using LOC as a measure for effort does not significantly affect the list of files being flagged, however, using LOC under-estimates the amount of effort required compared to our best effort predictor by approximately 66%. Conclusion Studies using effort-aware models should not assume that LOC is a good measure of effort. For the case of effort-aware bug prediction, using LOC provides results that are similar to combining complexity, churn, size and LOC as a proxy for effort when prioritizing the most risky files. However, we find that for the purpose of effort-estimation, using LOC may under-estimate the amount of effort required. © 2013 Elsevier B.V. All rights reserved.

    DOI: 10.1016/j.infsof.2013.06.002

  • Studying re-opened bugs in open source software

    Emad Shihab, Akinori Ihara, Yasutaka Kamei, Walid M. Ibrahim, Masao Ohira, Bram Adams, Ahmed E. Hassan, Ken Ichi Matsumoto

    Empirical Software Engineering   18 ( 5 )   1005 - 1042   2013.10

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    Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects - namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1-78.6 % and a recall in the range of 70.5-94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs. © 2012 Springer Science+Business Media, LLC.

    DOI: 10.1007/s10664-012-9228-6

  • 11種類のfault密度予測モデルの実証的評価 Reviewed

    小林 寛武, 戸田 航史, 亀井 靖高, 門田 暁人, 峯 恒憲, 鵜林 尚靖

    電子情報通信学会論文誌   2013.8

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  • 11種類のfault密度予測モデルの実証的評価

    小林 寛武, 戸田 航史, 亀井 靖高, 門田 暁人, 峯 恒憲, 鵜林 尚靖

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   96 ( 8 )   1892 - 1902   2013.8

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    Experimental Evaluation of Eleven Fault-Density Models

  • Using Alloy to Support Feature-Based DSL Construction for Mining Software Repositories Reviewed International journal

    Changyun Huang, Yasutaka Kamei, Kazuhiro Yamashita and Naoyasu Ubayashi

    International Workshop on Model-driven Approaches in Software Product Line Engineering and Workshop on Scalable Modeling Techniques for Software Product Lines (MAPLE/SCALE 2013)   2013.8

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  • An Authentication Method with Spatiotemporal Interval and Partial Matching Reviewed International journal

    Masateru Tsunoda, Kyohei Fushida, Yasutaka Kamei, Masahide Nakamura, Kohei Mitsui, Keita Goto, and Ken-ichi Matsumoto

    International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013)   2013.7

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  • An Empirical Study on Remote Lectures Using Video Conferencing Systems Reviewed International journal

    Tetsuya Oishi, Weiqiang Kong, Yasutaka Kamei, Norimichi Hiroshige, Naoyasu Ubayashi and Akira Fukuda

    International Conference on Frontiers in Education: CS and CE (FECS 2013)   2013.7

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  • Design Module: A Modularity Vision Beyond Code -Not Only Program Code But Also a Design Model Is a Module- Reviewed International journal

    Naoyasu Ubayashi and Yasutaka Kamei

    International Workshop on Modeling in Software Engineering (MiSE2013)   2013.5

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  • Domain Analysis for Mining Software Repositories -Towards Feature-based DSL Construction- Reviewed International journal

    Changyun Huang, Kazuhiro Yamashita, Yasutaka Kamei, Kenji Hisazumi and Naoyasu Ubayashi

    International Workshop on Product LinE Approaches in Software Engineering (PLEASE 2013)   2013.5

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  • An Experience Report on Remote Lecture Using Multi-point Control Unit Reviewed International journal

    Tetsuya Oishi, Yasutaka Kamei, Weiqiang Kong, Norimichi Hiroshige, Naoyasu Ubayashi, Akira Fukuda

    International Conference on Education and Teaching (ICET 2013)   2013.3

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  • UML-based Design and Verification Method for Developing Dependable Context-Aware Systems Reviewed International journal

    Naoyasu Ubayashi and Yasutaka Kamei

    International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2013)   2013.2

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  • A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction Reviewed International journal

    Akito Monden, Jacky Keung, Shuji Morisaki, Yasutaka Kamei and Kenichi Matsumoto

    Asia-Pacific Software Engineering Conference (APSEC 2012)   2012.12

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  • An Investigation on Software Bug Fix Prediction for Open Source Software Projects -A Case Study on the Eclipse Project- Reviewed International journal

    Akinori Ihara, Yasutaka Kamei, Akito Monden, Masao Ohira, Jacky Keung, Naoyasu Ubayashi and Kenichi Matsumoto

    International Workshop on Software Analysis, Testing and Applications (SATA2012)   2012.12

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  • UML4COP: UML-based DSML for Context-Aware Systems Reviewed International journal

    Naoyasu Ubayashi and Yasutaka Kamei

    International Workshop on Domain-Specific Modeling (DSM2012)   2012.10

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  • QORAL : External Domain-Specific Language for Mining Software Repositories. Reviewed International journal

    Hiroki Nakamura, Rina Nagano, Kenji Hisazumi, Yasutaka Kamei, Naoyasu Ubayashi and Akira Fukuda

    International Workshop on Empirical Software Engineering in Practice (IWESEP2012)   2012.10

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  • Locating Source Code to be Fixed based on Initial Bug Reports -A Case Study on the Eclipse Project Reviewed International journal

    Phiradet Bangcharoensap, Akinori Ihara, Yasutaka Kamei, Ken-ichi Matsumoto

    International Workshop on Empirical Software Engineering in Practice (IWESEP2012)   2012.10

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  • SMTソルバーを用いたコンテキスト指向プログラミングのためのデバッグ支援 Reviewed

    内尾 静, 鵜林 尚靖, 亀井 靖高

    コンピュータソフトウェア   2012.8

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  • Using the GPGPU for Scaling Up Mining Software Repositories Reviewed International journal

    Rina Nagano, Hiroki Nakamura, Yasutaka Kamei, Bram Adams, Kenji Hisazumi, Naoyasu Ubayashi and Akira Fukuda

    International Conference on Software Engineering (ICSE2012), Poster Session   2012.6

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  • Verifiable Architectural Interface for Supporting Model-Driven Development with Adequate Abstraction Level Reviewed International journal

    Naoyasu Ubayashi, Yasutaka Kamei

    International Workshop on Modeling in Software Engineering (MiSE2012)   2012.6

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  • An Extensible Aspect-oriented Modeling Environment for Constructing Domain-Specific Languages Reviewed

    Naoyasu Ubayashi, Yasutaka Kamei

    IEICE Transactions on Information and Systems   2012.4

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  • An extensible aspect-oriented modeling environment for constructing domain-specific languages

    Naoyasu Ubayashi, Yasutaka Kamei

    IEICE Transactions on Information and Systems   E95-D ( 4 )   942 - 958   2012.4

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    AspectM, an aspect oriented modeling (AOM) language, provides not only basic modeling constructs but also an extension mechanism called metamodel access protocol (MMAP) that allows a modeler to modify the metamodel. MMAP consists of metamodel extension points, extension operations, and primitive predicates for navigating the metamodel. Although the notion of MMAP is useful, it needs tool support. This paper proposes a method for implementing a MMAP based AspectM support tool. It consists of model editor, model weaver, and model verifier. We introduce the notion of edit-time structural reflection and extensible model weaving. Using these mechanisms, a modeler can easily construct domain-specific languages (DSLs). We show a case study using the AspectM support tool and discuss the effectiveness of the extension mechanism provided by MMAP. As a case study, we show a UML based DSL for describing the external contexts of embedded systems. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.

    DOI: 10.1587/transinf.E95.D.942

  • Architectural Point Mapping for Design Traceability Reviewed International journal

    Naoyasu Ubayashi and Yasutaka Kamei

    Foundations of Aspect-Oriented Languages workshop (FOAL2012)   2012.3

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  • ソフトウェア開発プロジェクトをまたがる fault-prone モジュール判別の試み : 18プロジェクトの実験から得た教訓

    藏本 達也, 亀井 靖高, 門田 暁人, 松本 健一

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   95 ( 3 )   425 - 436   2012.3

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    Fault-prone Module Prediction Across Software Development Projects : Lessons Learned from 18 Projects

  • dcNavi: デバッグを支援する関心事指向推薦システム Reviewed

    塩塚 大, 鵜林 尚靖, 亀井 靖高

    情報処理学会論文誌   2012.3

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  • OSSプロジェクトにおける開発者の活動量を用いたコミッター候補者予測

    伊原 彰紀, 亀井 靖高, 大平 雅雄, 松本 健一, 鵜林 尚靖

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   95 ( 2 )   237 - 249   2012.2

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    A Candidate Committer Prediction Based on Developer Activities in Open Source Software Projects

  • dcNavi:デバッグを支援する関心事指向推薦システム

    塩塚 大, 鵜林尚靖, 亀井 靖高

    情報処理学会論文誌   53 ( 2 )   631 - 643   2012.2

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    dcNavi: A Concern-oriented Recommendation System for Debugging Support
    Programmers tend to spend a lot of time debugging code. They check the erroneous phenomena, navigate the code, search the past bug fixes, and modify the code. If a sequence of these debug activities can be automated, programmers can use their time for more creative tasks. To deal with this problem, we propose dcNavi (Debug Concern Navigator), a concern-oriented recommendation system for debugging. The dcNavi provides appropriate hints to programmers according to their debug concerns by mining a repository containing not only program information but also test results and program modification history. In this paper, we evaluate the effectiveness of our approach in terms of the reusability of past bug fixes by using nine open source repositories created in the Eclipse plug-in projects.

  • 時空間情報と動作に基づく認証方法 Reviewed

    角田 雅照,伏田 享平,亀井 靖高,中村 匡秀,三井 康平,後藤 慶多,松本 健一

    知能と情報(日本知能情報ファジィ学会誌)   2011.12

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  • Translation Pattern of BPEL Process into Promela Code Reviewed International journal

    Ryosuke Nakashiro, Yasutaka Kamei, Naoyasu Ubayashi, Shin Nakajima, Akihito Iwai

    The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011)   2011.11

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  • An Analysis of Cost-overrun Projects using Financial Data and Software Metrics Reviewed International journal

    Hidetake Uwano, Yasutaka Kamei, Akito Monden, Ken-Ichi Matsumoto

    The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011)   2011.11

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  • Context Analysis Method for Embedded Systems ---Exploring a Requirement Boundary between a System and Its Context Reviewed International journal

    Naoyasu Ubayashi, Yasutaka Kamei, Masayuki Hirayama, Tetsuo Tamai

    3rd Workshop on Context-Oriented Programming (COP 2011)   2011.8

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  • Experimental Evaluation of Effect of Specifying a Focused Defect Classification in Software Inspection Reviewed

    Shuji Morisaki, Yasutaka Kamei, and Ken-ichi Matsumoto

    JSSST Journal   2011.8

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  • CJAdviser: SMT-based Debugging Support for ContextJ* Reviewed International journal

    Shizuka Uchio, Naoyasu Ubayashi, Yasutaka Kamei

    3rd Workshop on Context-Oriented Programming (COP 2011)   2011.7

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  • Debug Concern Navigator Reviewed International journal

    Masaru Shiozuka, Naoyasu Ubayashi, Yasutaka Kamei

    the 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE 2011)   2011.7

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  • Stepwise Context Boundary Exploration Using Guide Words Reviewed International journal

    Naoyasu Ubayashi, Yasutaka Kamei

    the 23rd International Conference on Advanced Information Systems Engineering (CAiSE 2011 Forum)   2011.6

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  • 開発者メトリックスに基づくソフトウェア信頼性の分析

    [マツ]本 真佑, 亀井 靖高, 門田 暁人, 松本 健一

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   93 ( 8 )   1576 - 1589   2010.8

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    Analyzing Software Reliability Based on Developer Metrics

  • 開発者メトリックスに基づくソフトウェア信頼性の分析

    [マツ]本 真佑, 亀井 靖高, 門田 暁人, 松本 健一

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   93 ( 8 )   1576 - 1589   2010.8

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    Analyzing Software Reliability Based on Developer Metrics

  • クローンメトリックスを用いた fault-prone モジュール判別の追実験

    亀井 靖高, 左藤 裕紀, 門田 暁人, 川口 真司, 上野 秀剛, 名倉 正剛, 松本 健一

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   93 ( 4 )   544 - 547   2010.4

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    A Replicated Experiment to Fault-Prone Module Detection with Clone Metrics

  • クローンメトリックスを用いた fault-prone モジュール判別の追実験

    亀井 靖高, 左藤 裕紀, 門田 暁人, 川口 真司, 上野 秀剛, 名倉 正剛, 松本 健一

    電子情報通信学会論文誌. D, 情報・システム = The IEICE transactions on information and systems (Japanese edition)   93 ( 4 )   544 - 547   2010.4

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    A Replicated Experiment to Fault-Prone Module Detection with Clone Metrics

  • 修正確認テスト規模の低減を目的としたコードレビュー手法

    田村 晃一, 亀井 靖高, 上野 秀剛, 森崎 修司, 松本 健一

    情報処理学会論文誌   50 ( 12 )   3074 - 3083   2009.12

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    A Code Review Technique to Reduce Fix Assurance Test Size
    In testing phases of software development projects, detected defects are fixed by modifying artifact including source code. Most of the detected defects require both test cases to confirm that the modification is correct and the modification does not cause new defects. In this paper, we propose a code reading technique in order to reduce size of such testing. The proposed method preferentially detects defects that potentially require larger size of testing by giving information that helps reviewer to estimate size of testing. In an evaluation experiment, the proposed technique reduces size of testing 2.1 times compared with test case based reading and 1.9 times compared with ad-hoc reading among 18 subjects including 6 commercial software developers.

  • Fault-proneモジュール判別におけるテスト工数割当てとソフトウェア信頼性のモデル化

    柿元 健, 門田 暁人, 亀井 靖高, 柗本 真佑, 松本 健一, 楠本 真二

    情報処理学会論文誌   50 ( 7 )   1716 - 1724   2009.7

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    Modeling of Test Effort Allocation and Software Reliability in Fault-prone Module Detection
    Various fault-prone detection models have been proposed to improve software reliability. However, while improvement of prediction accuracy was discussed, there was few discussion about how the models shuld be used in the field, i.e. how test effort should be allocated. Thus, improvement of software reliability by fault-prone module detection was not clear. In this paper, we proposed TEAR (Test Effort Allocation and software Reliability) model that represents the relationship among fault-prone detection, test effort allocation and software reliability. The result of simulations based on TEAR model showed that greater test effort should be allocated for fault-prone modules when prediction accuracy was high and/or when the number of faulty modules were small. On the other hand, fault-prone module detection should not be use when prediction accuracy was small or the number of faulty modules were large.

  • Fault-proneモジュール判別におけるテスト工数割当てとソフトウェア信頼性のモデル化

    柿元 健, 門田 暁人, 亀井 靖高, 柗本 真佑, 松本 健一, 楠本 真二

    情報処理学会論文誌   50 ( 7 )   1716 - 1724   2009.7

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    Modeling of Test Effort Allocation and Software Reliability in Fault-prone Module Detection
    Various fault-prone detection models have been proposed to improve software reliability. However, while improvement of prediction accuracy was discussed, there was few discussion about how the models shuld be used in the field, i.e. how test effort should be allocated. Thus, improvement of software reliability by fault-prone module detection was not clear. In this paper, we proposed TEAR (Test Effort Allocation and software Reliability) model that represents the relationship among fault-prone detection, test effort allocation and software reliability. The result of simulations based on TEAR model showed that greater test effort should be allocated for fault-prone modules when prediction accuracy was high and/or when the number of faulty modules were small. On the other hand, fault-prone module detection should not be use when prediction accuracy was small or the number of faulty modules were large.

  • ソフトウェアコンポーネント推薦における協調フィルタリングの効果

    亀井 靖高, 角田 雅照, 柿元 健, 大杉 直樹, 門田 暁人, 松本 健一

    情報処理学会論文誌   50 ( 3 )   1139 - 1143   2009.3

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    The Effect of Collaborative Filtering on Software Component Recommendation
    To clarify the effect of collaborative filtering (CF) on recommending highgenerality / low-generality software components, we experimentally verified two hypotheses; (1) the recommendation accuracy of CF for high-generality components is better than that of conventional methods (random algorithm and user average algorithm) and (2) the recommendation accuracy of CF for lowgenerality components is better than that of the conventional methods. We evaluated recommendation accuracy of CF with a dataset containing 29 open source software development projects (including 2,558 used components). As a result, the hypothesis (2) was supported, and the recommendation accuracy of CF showed better performance than the conventional methods and the median of NDPM was improved from 0.55 to 0.33 for low-generality components.

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Books

  • 大学教員のためのPBL実践ガイド

    福田 晃, 鵜林 尚靖, 荒木 啓二郎, 峯 恒憲, 日下部 茂, 金子 邦彦, 亀井 靖高, 廣重 法道, 中谷 薫, 辰巳 敬三(Role:Joint author)

    2013.3 

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    Language:Japanese   Book type:General book, introductory book for general audience

  • ソフトウェア工学の基礎XIX

    鵜林 尚靖, 亀井 靖高(Role:Joint author)

    2012.12 

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    Language:Japanese   Book type:General book, introductory book for general audience

Presentations

  • ソフトウェア変更に対するバグ予測モデルの精度評価

    亀井 靖高, 鵜林 尚靖

    電子情報通信学会技術報告  2012.3 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • 第34回ソフトウェア工学国際会議ICSE2012 参加報告

    亀井 靖高, 伊原 彰紀, 畑 秀明, 吉村 健太郎, 吉田 則裕

    情報処理学会研究報告, ソフトウェア工学研究会  2012.7 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • GPGPUを用いたリポジトリマイニングの高速化手法 ― プロセスメトリクスの算出への適用

    永野 梨南, 中村 央記, 亀井靖高, 久住 憲嗣, 鵜林尚靖, 福田晃

    SES2012  2012.8 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • リポジトリマイニング向けドメイン専用言語ArgyleJの開発と実証的評価

    山下 一寛, 亀井 靖高, 久住 憲嗣, 鵜林 尚靖

    SES2012  2012.8 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • クラッシュログを用いたソースコード不具合箇所の特定に向けた分析

    長本 貴光, 亀井 靖高, 伊原 彰紀, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2013.3 

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    Country:Japan  

  • PBLにおける発想法とロジカルシンキングの導入事例

    亀井 靖高, 細合 晋太郎, 大迫 周平, 川高 美由紀, 西川 忠行, 鵜林 尚靖, 福田 晃

    情報処理学会研究報告, ソフトウェア工学研究会  2013.7 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • リポジトリマイニングに対するHadoopの導入に向けた性能評価

    大坂 陽, 山下 一寛, 亀井 靖高, 鵜林 尚靖

    SES2013  2013.9 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • クラッシュリポジトリマイニング -ソースコード 欠陥箇所の特定に向けて-

    亀井 靖高, 長本 貴光, ラピュト シャシャンク, 小須田 光, 伊原 彰紀, 鵜林 尚靖

    ソフトウェア工学の基礎ワークショップ FOSE2013  2013.11 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • クラッシュレポートの送信頻度が不具合との関連付けに与える影響

    小須田 光, 亀井 靖高, 鵜林 尚靖

    ソフトウェア工学の基礎ワークショップ FOSE2014  2014.12 

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    Presentation type:Oral presentation (general)  

    Country:Japan  

  • 開発形態を考慮した企業内OSS事前品質評価手法

    中野 大扉, 松本 卓大, 山下 一寛, 亀井 靖高, 鵜林 尚靖, 高山 修一, 岩永 裕史, 岩崎 孝司

    情報処理学会研究報告, ソフトウェア工学研究会  2017.3 

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    Country:Japan  

  • Verification of BPEL Workflows Design using Model Checking International conference

    Ryosuke Nakashiro, Yasutaka Kamei, Naoyasu Ubayashi, Shin Nakajima, Akihito Iwai

    Joint Workshop on Software Science and Engineering  2011.6 

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    Presentation type:Oral presentation (general)  

    Country:Korea, Republic of  

  • SMTベースのCOPデバッグ支援

    内尾 静, 鵜林 尚靖, 亀井 靖高

    ソフトウェア工学の基礎ワークショップ FOSE 2011  2011.11 

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    Country:Japan  

  • オープンソースリポジトリのバグ修正履歴を再利用したデバッグ推薦の評価実験

    塩塚 大, 鵜林 尚靖, 亀井 靖高

    ソフトウェア工学の基礎ワークショップ FOSE 2011  2011.11 

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    Country:Japan  

  • 図書館問題2.0: ソフトウェア工学研究における共通問題例

    鵜林 尚靖, 亀井 靖高

    ウィンターワークショップ2012・イン・琵琶湖  2012.1 

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    Country:Japan  

  • アーキテクチャ点写像による設計・コード間の双方向追跡

    鵜林 尚靖, 亀井 靖高

    電子情報通信学会技術報告  2012.1 

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    Country:Japan  

  • リポジトリマイニング向けドメイン専用言語の設計と実装

    山下 一寛, 山本 大輔, 亀井 靖高, 久住 憲嗣, 鵜林 尚靖

    電子情報通信学会技術報告  2012.3 

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    Country:Japan  

  • Alloyによるリポジトリマイニング向けドメイン専用言語の構築支援

    黄 長贇, 中城 亮祐, 山下 一寛, 亀井 靖高, 久住 憲嗣, 鵜林 尚靖

    電子情報通信学会技術報告  2012.7 

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    Country:Japan  

  • GPGPUを用いたリポジトリマイニングのための外部ドメイン専用言語 QORALの提案

    中村 央記, 永野 梨南, 久住 憲嗣, 亀井靖高, 鵜林尚靖, 福田晃

    電子情報通信学会技術報告  2012.5 

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    Country:Japan  

  • OSSにおけるパッチ検証期間の短縮の方策

    戸田 航史, 亀井 靖高

    ソフトウェアエンジニアリングシンポジウム2012 併設ワークショップ 「ソフトウェア開発マネジメントの実践と課題」  2012.8 

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    Country:Japan  

  • Archpoint and Archmapping: Bidirectional Traceability between Design and Code International conference

    Naoyasu Ubayashi and Yasutaka Kamei

    Korea-Japan Joint Workshop on ICT  2012.9 

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    Country:Japan  

  • Bug Localization based on Textual Similarity with Bug Report and Code Property: A Case Study on Eclipse Project International conference

    Phiradet Bangcharoensap, Akinori Ihara, Yasutaka Kamei, Ken-ichi Matsumoto

    The 5th Thailand-Japan International Academic Conference (TJIA 2012)  2012.10 

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    Country:Japan  

  • MODULARITY:aosd.2012 参加報告

    紙名 哲生, 亀井 靖高, 青谷 知幸

    情報処理学会研究報告, ソフトウェア工学研究会  2012.10 

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    Country:Japan  

  • リポジトリマイニングの進化に対応した分析ツールE-CUBEの構築

    山下 一寛, 亀井 靖高, 久住 憲嗣, 鵜林 尚靖

    電子情報通信学会技術報告, 知能ソフトウェア工学研究会  2012.12 

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    Country:Japan  

  • ソフトウェア開発におけるチーム単位での生産性の分析について

    戸田 航史, 亀井 靖高

    ウィンターワークショップ2013・イン・那須  2013.1 

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    Country:Japan  

  • ソフトウェアバグ予測のためのモジュール分類と選定

    盛 慎, 門田 暁人, 亀井 靖高, 松本 健一

    電子情報通信学会技術報告, 知能ソフトウェア工学研究会  2013.1 

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    Country:Japan  

  • ソフトウェア工学研究のための共通問題集の提案

    鵜林 尚靖, 亀井 靖高

    ウィンターワークショップ2013・イン・那須  2013.1 

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    Country:Japan  

  • リポジトリマイニングに対するHadoopの性能評価

    大坂 陽, 山下 一寛, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2013.3 

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    Country:Japan  

  • MSR2013 参加報告

    山下 一寛, 角田 雅照, 伊原 彰紀, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2013.7 

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    Country:Japan  

  • 滑らかな設計抽象化

    鵜林 尚靖, 艾 迪, 細合 晋太郎, 亀井 靖高

    ソフトウェアサイエンス研究会  2013.7 

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    Country:Japan  

  • PBLへのDaaS開発環境の導入事例

    細合 晋太郎, 亀井 靖高, 大迫 周平, 井垣 宏, 鵜林 尚靖, 福田 晃

    ソフトウェアサイエンス研究会  2013.7 

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    Country:Japan  

  • PBLにおけるデザイン思考の導入事例

    大迫 周平, 亀井 靖高, 細合 晋太郎, 加藤 公敬, 石塚 昭彦, 坂口 和敏, 川高 美由紀, 森田 昌嗣, 鵜林 尚靖, 福田 晃

    情報処理学会研究報告, ソフトウェア工学研究会  2013.10 

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    Country:Japan  

  • ソフトウェア開発プロジェクトをまたがるJust-In-Timeバグ予測の実験的評価

    福島 崇文, 亀井 靖高, 鵜林尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2013.10 

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    Country:Japan  

  • Chromium Projectにおけるレビュアーとパッチ開発者の関係がレビュー効率に与える影響の分析

    戸田 航史, 濱崎 一樹, 亀井 靖高, 吉田 則裕

    ソフトウェア工学の基礎ワークショップ FOSE2013  2013.11 

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    Country:Japan  

  • 脳血流計測に基づくプログラム理解行動の定量化

    中川 尊雄, 亀井 靖高, 上野 秀剛, 門田 暁人, 松本 健一

    ソフトウェア工学の基礎ワークショップ FOSE2013  2013.11 

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  • E-CUBE: An Analysis Tool to Support Three Types of Evolution in Mining Software Repositories

    Kazuhiro Yamashita, Yasutaka Kamei, Kenji Hisazumi, Naoyasu Ubayashi

    International Workshop on Information & Communication Technologies (IWICT2013)  2013.12 

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    Country:Japan  

  • リポジトリマイニング工程への高速化手法適用に向けた初期実験

    山下 一寛, 亀井 靖高, 鵜林 尚靖

    ウィンターワークショップ2014・イン・大洗  2014.1 

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    Country:Japan  

  • OSS 開発におけるパッチレビュープロセス追跡技術の提案

    大坂 陽, 伊原 彰紀, 亀井 靖高, 鵜林 尚靖

    ウィンターワークショップ2014・イン・大洗  2014.1 

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  • コードクローンメトリクスの差異がfault-proneモジュール判別に与える影響

    角田 雅照, 梶村 和輝, 亀井 靖高, 沢田 篤史

    ウィンターワークショップ2014・イン・大洗  2014.1 

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    Country:Japan  

  • 変更レベルに着目したバグ予測支援ツールの設計と実装

    田中 秀太郎, 山下 一寛, 亀井 靖高, 鵜林 尚靖

    ソフトウェアサイエンス研究会  2014.1 

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    Country:Japan  

  • 開発メーリングリストマイニングの前処理システムの開発

    川島関夫, 亀井靖高, 鵜林尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2014.3 

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  • クラッシュレポートが不具合修正に与える影響の分析

    小須田光, 亀井靖高, 伊原彰紀, 鵜林尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2014.3 

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  • DSLラインエンジニアリング支援環境の設計

    黄 長贇, 亀井 靖高, 鵜林 尚靖

    ソフトウェアサイエンス研究会  2014.3 

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    Country:Japan  

  • パッチレビュープロセスにおけるパッチ作成者の継続性の違い

    大坂 陽, 伊原 彰紀, 亀井 靖高, 松本 健一, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2014.5 

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    Country:Japan  

  • PBLにおけるデザイン思考適用の効果と課題

    大迫 周平, 亀井 靖高, 細合 晋太郎, 加藤 公敬, 石塚 昭彦, 坂口 和敏, 川高 美由紀, 森田 昌嗣, 鵜林 尚靖, 福田 晃

    情報処理学会研究報告, ソフトウェア工学研究会  2014.5 

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    Country:Japan  

  • IoTシステムを題材としたPBLの導入提案

    細合 晋太郎, 石田 繁巳, 亀井 靖高, 大迫 周平, 井垣 宏, 鵜林 尚靖, 福田 晃

    情報処理学会研究報告, ソフトウェア工学研究会  2014.7 

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  • 設計抽象化のためのリファクタリングパターン

    艾 迪, 鵜林 尚靖, 李 沛源, 李 宇寧, 細合 晋太郎, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2014.7 

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  • ソフトウェア工学研究に対するHPC利用の効果 -コードクローン研究の事例を通して-

    大坂 陽, 亀井 靖高, 堀田 圭佑, 鵜林 尚靖

    SES2014 ポスター  2014.9 

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  • 対話的なDSL構築環境Argyle

    黄 長贇, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    SES2014 ポスター  2014.9 

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    Country:Japan  

  • IoTを題材としたPBLの実施と分析

    細合 晋太郎, 石田 繁巳, 亀井 靖高, 大迫 周平, 井垣 宏, 鵜林 尚靖, 福田 晃

    日本ソフトウェア科学会 第31回大会  2014.9 

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  • テキストマイニングによるPBL発表会評価アンケート傾向分析

    大迫 周平, 孔 維強, 亀井 靖高, 細合 晋太郎, 石田 繁巳, 鵜林 尚靖, 福田 晃

    日本ソフトウェア科学会 第31回大会  2014.9 

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  • 大規模OSS開発における不具合修正時間の短縮化を目的としたバグトリアージ手法

    柏 祐太郎, 大平 雅雄, 阿萬 裕久, 亀井 靖高

    SES2014  2014.9 

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  • Chromiumのgitリポジトリから算出可能な工数の見積もり手法の定量的評価

    戸田 航史, 亀井 靖高

    ソフトウェア工学の基礎ワークショップ FOSE2014 ポスター  2014.12 

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  • 不確かさを包容するJavaプログラミング・テスト環境

    深町 拓也, 鵜林 尚靖, 亀井 靖高

    ソフトウェア工学の基礎ワークショップ FOSE2014 ポスター  2014.12 

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  • 設計抽象化のためのリファクタリング支援

    艾 迪, 鵜林 尚靖, 李 沛源, 李 宇寧, 細合 晋太郎, 亀井 靖高

    ソフトウェア工学の基礎ワークショップ FOSE2014  2014.12 

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    Country:Japan  

  • OSSプロジェクトにおけるコミッターの承認に対する動機の理解

    伊原 彰紀, 亀井 靖高, 大平 雅雄, Bram Adams, 松本 健一

    ソフトウェア工学の基礎ワークショップ FOSE2014  2014.12 

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  • 異なるコードクローンメトリクスを用いた欠陥モジュール予測の試み

    角田 雅照, 梶村 和輝, 亀井 靖高, 沢田 篤史

    ソフトウェア工学の基礎ワークショップ FOSE2014  2014.12 

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  • コードクローン解析に対するスーパーコンピュータ導入に向けた試行実験

    大坂 陽, 亀井 靖高, 堀田 圭佑, 鵜林 尚靖

    ソフトウェアサイエンス研究会  2015.1 

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  • Just-In-Time欠陥予測支援ツール anko

    田中 秀太郎, 福島 崇文, 山下 一寛, 亀井 靖高, 鵜林 尚靖

    ソフトウェアサイエンス研究会  2015.1 

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  • 不確かさを包容するJavaプログラミング環境

    深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2015.3 

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  • オープンソースソフトウェアの進化における特定OS向け欠陥修正コミットの分析

    松本 卓大, 大坂 陽, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2015.3 

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  • ソフトウェア進化におけるワークアイテムとコミット数に関する調査

    三浦 圭裕, 福島 崇文, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2015.3 

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  • 社会としてのOSSプロジェクトを解析するソーシャルネットワークマイニングツールColi

    川島 関夫, 亀井 靖高, 鵜林 尚靖

    ソフトウェアサイエンス研究会  2015.3 

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  • 抽象化を考慮したデータフロートレーサビリティ

    郭 衆小, 鵜林 尚靖, 艾 迪, 李 沛源, 李 宇寧, 深町 拓也, 細合 晋太郎, 亀井 靖高

    ソフトウェアサイエンス研究会  2015.3 

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  • 不確かさを包容した開発プロセスとその支援環境iArch-U

    深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2015.7 

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  • 第37回ソフトウェア工学国際会議ICSE2015参加報告

    深町 拓也, 亀井 靖高, 鵜林 尚靖, 花川 典子, 青山 幹雄

    情報処理学会研究報告, ソフトウェア工学研究会  2015.7 

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  • 商用Android ソフトウェア開発環境におけるコードレビュー統計の実証的研究

    島垣 潤二, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2015.7 

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  • Reference Couplingを用いたソフトウェアエコシステムに関する初期実験

    山下 一寛, 亀井 靖高, 鵜林 尚靖

    SES2015 ポスター  2015.9 

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  • マルチプラットフォーム向けソフトウェアに関する特定OS向け欠陥修正コミットの分析

    松本 卓大, 亀井 靖高, Shane McIntosh, 鵜林 尚靖

    SES2015  2015.9 

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  • プロジェクトの継続性の計測に向けた初期研究

    山下 一寛, 亀井 靖高, 鵜林 尚靖

    ソフトウェア工学の基礎ワークショップ FOSE2015 ポスター  2015.11 

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  • 不確かさを包容するソフトウェア開発プロセス

    深町 拓也,鵜林 尚靖,細合 晋太郎,亀井 靖高

    FOSE2015  2015.11 

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  • 再現性のあるアソシエーションルールの選定 ~ソフトウェアバグ予測を題材として~

    渡部 恭史, 門田 暁人, 亀井 靖高, 森崎 修司

    情報処理学会研究報告, ソフトウェア工学研究会  2015.12 

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  • ユーザ障害情報によるソースコード欠陥箇所予測ツール

    小須田 光, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2015.12 

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  • 製品開発におけるOSS導入のためのOSS事前評価に向けた初期調査

    松本 卓大, 山下 一寛, 亀井 靖高, 鵜林 尚靖, 深海 竜也, 岩崎 孝司

    情報処理学会研究報告, ソフトウェア工学研究会  2015.12 

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  • コミットログを用いたOSS開発における不確かさに関する実証分析

    山下 一寛, 江 冠達, 深町 拓也, 亀井 靖高, 鵜林 尚靖

    ソフトウェアサイエンス研究会  2016.1 

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  • DebugConcierge: クラウド知識に基づいたデバッグ支援環境

    廣瀬 賢幸, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, 渡邉 卓也

    ウィンターワークショップ2016・イン・逗子  2016.2 

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  • 不確かさを包容するテスト支援

    渡辺 啓介, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, 渡邉 卓也

    ウィンターワークショップ2016・イン・逗子  2016.2 

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  • CodeConcierge:クラウド知識に基づいたプログラミング支援

    高橋 裕太, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, 渡邉 卓也

    ウィンターワークショップ2016・イン・逗子  2016.2 

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  • AspectJによる不確かさを包容した単体テスト環境

    渡辺 啓介, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    知能ソフトウェア工学研究会  2016.3 

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  • Gitを用いた不確かさのマネジメント

    深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    知能ソフトウェア工学研究会  2016.3 

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  • Stack Overflowを用いたデバッグの有用性に関する実証分析

    荻原 由輝, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    ソフトウェアサイエンス研究会  2016.3 

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  • クラウド知識に基づいたプログラミング支援環境CodeConcierge

    高橋 裕太, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    ソフトウェアサイエンス研究会  2016.3 

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  • LTSA連携による不確かさを包容した自動モデル検査

    中村 隼也, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2016.3 

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  • 開発者の意図に沿ったデバッグ支援環境 DebugConcierge

    廣瀬 賢幸, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2016.3 

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  • Deep Learningのリポジトリマイニングへの適用に向けた初期研究

    松本 卓大, 山下 一寛, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2016.7 

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  • Git連携による不確かさマネジメントシステム

    深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    SES2016  2016.8 

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  • 感情分析によるOSSプロジェクト中断の予測に向けた調査

    山下 一寛, 亀井 靖高, 鵜林 尚靖

    SES2016  2016.8 

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  • 原型分析を用いたソフトウェアバグ分析

    瀧本 恵介, 門田 暁人, 尾上 紗野, 畑 秀明, 亀井 靖高

    ソフトウェアサイエンス研究会  2016.10 

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  • 宣言的な可変性記述によるA/B テストの自動化

    渡辺 啓介, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高

    FOSE2016  2016.12 

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  • 開発現場への導入を想定した OSS 事前評価手法確立に向けた調査

    松本 卓大, 山下 一寛, 亀井 靖高, 鵜林 尚靖, 大浦 雄太, 岩崎 孝司, 高山 修一,

    FOSE2016  2016.12 

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  • コードクローン検出技法を利用したバグ修正テンプレートの自動生成

    廣瀬 賢幸, 鵜林 尚靖, 亀井 靖高

    FOSE2016  2016.12 

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  • OSSにおける開発知識の遍在に関する実証分析

    西中 隆志郎, 山下 一寛, 鵜林 尚靖, 亀井 靖高

    ウィンターワークショップ2017・イン・飛騨高山  2017.1 

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  • 不確かさの発生過程に関する実証分析

    村岡 北斗, 深町 拓也, 山下 一寛, 鵜林 尚靖, 亀井 靖高

    ウィンターワークショップ2017・イン・飛騨高山  2017.1 

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  • 開発者の離脱理由に着目した OSSプロジェクトの持続性理解

    山下 一寛, 亀井 靖高, 鵜林 尚靖

    ウィンターワークショップ2017・イン・飛騨高山  2017.1 

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  • OSSプロジェクトにおける不確かさに関する実証分析 ~ なぜ不確かさは生まれ、いつ解消されるのか? ~

    村岡 北斗, 深町 拓也, 山下 一寛, 鵜林 尚靖, 亀井 靖高

    ソフトウェアサイエンス研究会  2017.3 

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  • Git開発履歴情報に基づく不確かさの可視化

    村岡 北斗, 村本 大起, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    情報処理学会研究報告, ソフトウェア工学研究会  2017.9 

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  • OSSプロジェクトにおけるTangledコミットの実証分析

    三浦 圭裕, 亀井 靖高, 鵜林 尚靖, 佐藤 亮介

    日本ソフトウェア科学会, 第34回全国大会  2017.9 

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  • 高階関数型言語におけるランキング関数の回帰推定

    村本 大起, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    日本ソフトウェア科学会, 第34回全国大会ウィンターワークショップ2018・イン・宮島  2018.1 

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  • 機械学習を用いたCoq上の命題論理の自動証明

    金原 雅典, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    日本ソフトウェア科学会, 第34回全国大会ウィンターワークショップ2018・イン・宮島  2018.1 

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  • 不確かさ分析用公開データベースの作成に向けて

    村岡 北斗, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    日本ソフトウェア科学会, 第34回全国大会ウィンターワークショップ2018・イン・宮島  2018.1 

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  • セキュリティバグ修正におけるCVE情報の実証実験に向けて

    中野 大扉, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    日本ソフトウェア科学会, 第34回全国大会ウィンターワークショップ2018・イン・宮島  2018.1 

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  • CVE記述が存在するセキュリティバグ修正に関する調査

    中野 大扉, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2018.3 

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  • 機械学習によるCoq上の命題論理の自動証明に関する研究

    金原 雅典, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2018.3 

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  • 関数型言語における停止性検証のためのランキング関数の回帰推定

    村本 大起, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2018.3 

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  • OSS事前評価による開発リスク特定の取組み

    岩崎 孝司, 高山 修一, 岩永 裕史, 鵜林 尚靖, 亀井 靖高

    SS2017  2017.6 

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  • ソフトウェア開発における不確かさに着目した OSS コミットログ解析

    村本 大起, 江 冠逹, 村岡 北斗, 深町 拓也, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    SES2017  2017.9 

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  • ソフトウェア開発履歴情報からのAPI Q&A知識の自動抽出

    西中 隆志郎, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    FOSE2017  2017.11 

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  • Stack Overflowを利用した自動バグ修正の検討

    廣瀬 賢幸, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    FOSE2017  2017.11 

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  • OSS事前品質評価における重み付け手法の実証実験

    中野 大扉, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖, 高山 修一, 岩永 裕史, 岩崎 孝司

    FOSE2017  2017.11 

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  • ソースコード変更履歴によるStack Overflow記事の充実に向けて

    西中 隆志郎, 佐藤 亮介, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2018.12 

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  • StackOverflow投稿を用いた深層学習による自動バグ修正にむけて

    高橋 裕太, 佐藤 亮介, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2018.12 

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  • 関数型プログラムの条件式のための反例を用いた自動修正

    松井 健, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2019.3 

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  • 企業内ソースコードに対する自動バグ修正技術適用の試み

    池田 翔, 中野 大扉, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖, 吉武 浩, 矢川 博文

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2019.3 

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  • シーケンス変換を用いた命題論理の自動証明

    金原 雅典, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    ソフトウェア科学会, プログラミングおよびプログラミング言語ワークショップ  2019.3 

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  • ソースコード修正履歴が自動バグ修正の結果に与える影響の分析

    首藤 巧, 亀井 靖高, 鵜林 尚靖, 佐藤 亮介

    情報処理学会研究報告, ソフトウェア工学研究会  2019.3 

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  • Self-Admitted Technical Debtの存在期間・除去人物についての追実験

    西川 諒真, 西中 隆志郎, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2019.3 

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  • 敵対的サンプルに対するニューラルネットワークモデルの学習無し修正とその評価

    松井 健, 鵜林 尚靖, 佐藤 亮介, 亀井 靖高

    日本ソフトウェア科学会第36回大会  2019.8 

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  • Revertに着目した不確かさに関する実証的分析

    村岡 北斗, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    SES2019  2019.8 

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  • 集合知を用いた深層学習による自動バグ修正

    高橋 裕太, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介

    SES2019  2019.8 

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  • ソースコードの修正履歴が自動バグ修正の結果に与える影響の分析

    首藤 巧, 亀井 靖高, 鵜林 尚靖, 佐藤 亮介

    FOSE2019  2019.11 

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  • 静的解析ツールの警告に対する自動バグ修正技術の適用と初期評価

    浅田 翔, 首藤 巧, 山手 響介, 佐藤 亮介, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2020.3 

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  • 修正ソースコードの特徴が自動バグ修正に与える影響の分析

    中村 司, 池田 翔, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2020.3 

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  • OSSプロジェクトにおけるREADME.mdファイルの作成の支援

    清水 一輝, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2020.3 

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  • 文法エラーに対する自動バグ修正ツールの性能評価

    松尾 春紀, 池田 翔, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2020.3 

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  • 開発者によるバグ限局を考慮した自動バグ修正への影響分析

    山手 響介, 首藤 巧, 浅田 翔, 佐藤 亮介, 亀井 靖高, 鵜林 尚靖

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2020.3 

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  • ランキング関数の回帰推定による関数型言語の停止性検証

    村本 大起, 佐藤 亮介, 鵜林 尚靖, 亀井 靖高

    情報処理学会研究報告, プログラミング研究会  2020.3 

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  • 静的解析ツールの警告に対する自動バグ修正技術の適用と初期評価

    浅田 翔, 首藤 巧, 山手 響介, 佐藤 亮介, 亀井 靖高, 鵜林 尚靖

    ウィンターワークショップ2020・イン・京都  2020.1 

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  • 開発者によるバグ限局が自動バグ修正の結果に与える影響の分析

    山手 響介, 首藤 巧, 浅田 翔, 佐藤 亮介, 亀井 靖高, 鵜林 尚靖

    ウィンターワークショップ2020・イン・京都  2020.1 

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  • コンテナ仮想化技術におけるSelf-Admitted Technical Debtの調査

    東 英明, 柗本 真佑, 亀井 靖高, 楠本 真二

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2020.10 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • 自動バグ修正研究のためのプラットフォームjProphetの開発について

    首藤 巧, 亀井 靖高, 鵜林 尚靖, 佐藤 亮介, 浅田 翔, 山手 響介

    情報処理学会研究報告, ソフトウェア工学研究会  2020.11 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • Jupyter Notebookのための実行ログを用いた再現性支援ツールの提案

    松原 直利, 松井 健, 鵜林 尚靖, 亀井 靖高

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • プログラミング教育支援に向けた深層学習を用いた類似問題検索

    山本 大貴, 松尾 春紀, 沖野 健太郎, 亀井 靖高, 鵜林 尚靖

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • 修正履歴を用いた機械翻訳技術による自動バグ修正の性能評価

    秋山 楽登, 中村 司, 亀井 靖高, 鵜林 尚靖

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • 自動生成されたテストケースが自動バグ修正の結果に与える影響の分析

    松田 雄河, 山手 響介, 亀井 靖高, 鵜林 尚靖

    電子情報通信学会技術報告, ソフトウェアサイエンス研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • プログラム自動生成に向けたソースコード検索器の性能評価

    沖野 健太郎, 松尾 春紀, 山本 大貴, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • DockerfileにおけるSelf-Admitted Technical Debt の削除

    新堂 風, 東 英明, 柗本 真佑, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • リファクタリングがテストコードに与える影響の定量的調査

    清水 一輝, 柏 祐太郎, 亀井 靖高, 鵜林 尚靖

    情報処理学会研究報告, ソフトウェア工学研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

  • RNNの抽象化モデルに対するバグ限局とその評価

    石本 優太, 松井 健, 鵜林 尚靖, 亀井 靖高

    情報処理学会研究報告, ソフトウェア工学研究会  2021.3 

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    Language:Japanese   Presentation type:Oral presentation (general)  

    Country:Japan  

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MISC

  • ソフトウェア定量的管理にかかわる学術研究事例 (特集 ソフトウェア開発の定量的管理)

    野中 誠, 亀井 靖高, 大平 雅雄

    SEC Journal   2017.12

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)  

  • ビッグデータ時代のソフトウェア・アナリティクス

    亀井 靖高, 島垣 潤二, 野中 誠

    情報処理(ソフトウェア工学の最前線 〜ソフトウェアが社会のすべてを定義する時代〜)   2017.8

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)  

Professional Memberships

  • 情報処理学会

  • 電子情報通信学会

  • 米国電気電子学会(IEEE)

  • Association for Computing Machinery (ACM)

  • ソフトウェア科学会

Committee Memberships

  • International Conference on Mining Software Repositories (MSR)   Steering Committee   Foreign country

    2016.5 - 2020.5   

  • International Conference on Software Analysis, Evolution, and Reengineering (SANER)   Steering Committee   Foreign country

    2016.2 - 2020.3   

Academic Activities

  • 情報処理学会 「ソフトウェア工学」特集号

    2024.3 - 2025.2

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    Type:Academic society, research group, etc. 

  • Program Committee International contribution

    International Conference on Software Engineering (ICSE), 2022@Technical Track  ( Spain ) 2022.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Engineering (ICSE), 2021@Technical Track  ( Spain ) 2021.5

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    Type:Competition, symposium, etc. 

  • 情報処理学会 「ソフトウェア工学」特集号

    2021.3 - 2022.2

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    Type:Academic society, research group, etc. 

  • Science of Computer Programming (Journal) International contribution

    2021.1 - 2026.2

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    Type:Academic society, research group, etc. 

  • Automated Software Engineering (Journal) International contribution

    2021.1 - 2026.2

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    Type:Academic society, research group, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2021

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:26

    Number of peer-reviewed articles in Japanese journals:4

    Proceedings of International Conference Number of peer-reviewed papers:45

    Proceedings of domestic conference Number of peer-reviewed papers:4

  • Program Committee International contribution

    International Symposium on the Foundations of Software Engineering (FSE), 2020@Research Track  ( UnitedStatesofAmerica ) 2020.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE), 2020@Research Track  ( UnitedStatesofAmerica ) 2020.11

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェアエンジニアリングシンポジウム (SES 2020)  ( Japan ) 2020.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Engineering (ICSE), 2020@Artifacts Evaluation Committee, 2020@Software Engineering in Practice Track, 2020@New Ideas and Emerging Results Track,  ( SouthKorea ) 2020.7

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Mining Software Repositories (MSR), 2020@Research Track  ( SouthKorea ) 2020.6

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    Type:Competition, symposium, etc. 

  • 情報処理学会 「ソフトウェア工学」特集号

    2020.3 - 2021.2

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    Type:Academic society, research group, etc. 

  • Publicity and Social Media Co-Chairs International contribution

    International Conference on Software Analysis, Evolution and Reengineering (SANER 2020)  ( Canada ) 2020.2 - 2019.2

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    Type:Competition, symposium, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2020

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:6

    Number of peer-reviewed articles in Japanese journals:2

    Proceedings of International Conference Number of peer-reviewed papers:45

    Proceedings of domestic conference Number of peer-reviewed papers:4

  • Program Committee International contribution

    International Conference on Automated Software Engineering (ASE), 2019@Tool Demonstration  ( UnitedStatesofAmerica ) 2019.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Software Visualization (VISSOFT), 2019@NIER/Tool Demo  ( UnitedStatesofAmerica ) 2019.9 - 2019.10

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE), 2019@Research Track  ( Brazil ) 2019.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Symposium on Empirical Software Engineering and Measurement (ESEM), 2019@Emerging Results and Vision  ( Brazil ) 2019.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Mining Software Repositories (MSR), 2019@Research Track  ( Canada ) 2019.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Engineering (ICSE), 2019@Workshop Selection Committee  ( Canada ) 2019.5

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    Type:Competition, symposium, etc. 

  • Tools Track Co-Chairs International contribution

    International Conference on Program Comprehension (ICPC 2019)  ( Canada ) 2019.5

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    Type:Competition, symposium, etc. 

  • 情報処理学会 「ソフトウェア工学」特集号

    2019.3 - 2020.2

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    Type:Academic society, research group, etc. 

  • Program Committee International contribution

    International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2019@Research Track  ( China ) 2019.2

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    Type:Competition, symposium, etc. 

  • Far East Co-Chairs International contribution

    International Conference on Software Analysis, Evolution and Reengineering (SANER 2019)  ( China ) 2019.2

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    Type:Competition, symposium, etc. 

  • Tutorials Co-Chairs International contribution

    Asia-Pacific Software Engineering Conference (APSEC 2018)  ( Japan ) 2018.12

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2018), Research Track  ( Spain ) 2018.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Automated Software Engineering (ASE 2018)  ( France ) 2018.9

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    Type:Competition, symposium, etc. 

  • PC Co-Chair International contribution

    International Working Conference on Mining Software Repositories (MSR2018)  ( Sweden ) 2018.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2018@Research Track  ( Italy ) 2018.3

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    Type:Competition, symposium, etc. 

  • 情報処理学会 「ソフトウェア工学」特集号

    2018.3 - 2019.2

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    Type:Academic society, research group, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2018

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:14

    Number of peer-reviewed articles in Japanese journals:2

    Proceedings of International Conference Number of peer-reviewed papers:42

    Proceedings of domestic conference Number of peer-reviewed papers:4

  • プログラム委員

    ソフトウェア工学の基礎ワークショップ(FOSE)2017  ( Japan ) 2017.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Automated Software Engineering (ASE 2017), Expert Review Panel (ERP)  ( UnitedStatesofAmerica ) 2017.10 - 2017.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Automated Software Engineering (ASE 2017), Tool Demonstration  ( UnitedStatesofAmerica ) 2017.10 - 2017.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Software Visualization (VISSOFT), 2017@Research Track  ( China ) 2017.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2017), Research Track  ( China ) 2017.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2017), Artifacts Track  ( China ) 2017.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Symposium on the Foundations of Software Engineering (FSE 2017), Artifacts Track  ( Germany ) 2017.9

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェアエンジニアリングシンポジウム2017  ( Japan ) 2017.8 - 2017.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Quality, Reliability and Security (QRS), 2017@Research Track  ( CzechRepublic ) 2017.7

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Program Comprehension (ICPC), 2017@Tool Demenstration Track  ( Argentina ) 2017.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Program Comprehension (ICPC), 2017@Research Track  ( Argentina ) 2017.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Engineering (ICSE2017), Poster Track  ( Argentina ) 2017.5

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    Type:Competition, symposium, etc. 

  • 座長(Chairmanship) International contribution

    14th International Conference on Mining Software Repositories  ( Buenos Aires, Argentina Argentina ) 2017.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Mining Software Repositories (MSR), 2017@Research Track  ( Argentina ) 2017.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Analysis, Evolution, and Reengineering (SANER), 2017@Research Track  ( Austria ) 2017.2

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    Type:Competition, symposium, etc. 

  • 情報処理学会 「ソフトウェア工学」特集号

    2017.2 - 2018.2

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    Type:Academic society, research group, etc. 

  • Screening of academic papers

    Role(s): Peer review

    2017

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    Type:Peer review 

    Number of peer-reviewed articles in foreign language journals:8

    Number of peer-reviewed articles in Japanese journals:5

    Proceedings of International Conference Number of peer-reviewed papers:45

    Proceedings of domestic conference Number of peer-reviewed papers:4

  • Program Committee International contribution

    International Workshop on Empirical Software Engineering in Practice  ( Japan ) 2016.12

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェア工学の基礎ワークショップ(FOSE)2016  ( Japan ) 2016.12

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Symposium on the Foundations of Software Engineering (FSE 2016), Artifacts Track  ( UnitedStatesofAmerica ) 2016.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2016), Research Track  ( UnitedStatesofAmerica ) 2016.10

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2016), Tool Demonstration  ( UnitedStatesofAmerica ) 2016.10

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2016), Artifacts Track  ( UnitedStatesofAmerica ) 2016.10

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Workshop on Software Analytics (SWAN 2016)  ( UnitedStatesofAmerica ) 2016.8 - 2016.9

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェアエンジニアリングシンポジウム2016  ( Japan ) 2016.8 - 2016.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Program Comprehension (ICPC), 2016@Short Paper Track  ( UnitedStatesofAmerica ) 2016.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Mining Software Repositories (MSR), 2016@Research Track  ( UnitedStatesofAmerica ) 2016.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Mining Software Repositories (MSR), 2016@Data Challenge  ( UnitedStatesofAmerica ) 2016.5

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    Type:Competition, symposium, etc. 

  • PC Co-Chair International contribution

    International Conference on Software Analysis, Evolution, and Reengineering (SANER2016)  ( Japan ) 2016.3

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェア工学の基礎ワークショップ(FOSE)2015  ( Japan ) 2015.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Maintenance and Evolution (ICSME2015), Early Research Achievements (ERA) Track  ( Germany ) 2015.9 - 2015.10

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Source Code Analysis and Manipulation (SCAM2015)  ( Germany ) 2015.9

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェアエンジニアリングシンポジウム2015  ( Japan ) 2015.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Program Comprehension (ICPC), Tool Demonstration Track  ( Italy ) 2015.5

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    Type:Competition, symposium, etc. 

  • 座長(Chairmanship) International contribution

    37th ACM/IEEE International Conference on Software Engineering  ( Firenze, Italy Italy ) 2015.5 - 2016.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Conference on Software Engineering (ICSE2015), Software Engineering In Practice  ( Italy ) 2015.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Working Conference on Mining Software Repositories (MSR2015)  ( Italy ) 2015.5

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    Type:Competition, symposium, etc. 

  • PC Chair International contribution

    International Working Conference on Mining Software Repositories (MSR2015), Data showcase Track  ( Italy ) 2015.5

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Conference on Software Analysis, Evolution, and Reengineering (SANER2015)  ( Canada ) 2015.3

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    Type:Competition, symposium, etc. 

  • 座長(Chairmanship) International contribution

    22nd IEEE International Conference on Software Analysis, Evolution, and Reengineering  ( Japan ) 2015.2 - 2015.3

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    Type:Competition, symposium, etc. 

  • 共同実行委員長

    ウィンターワークショップ・イン・宜野湾 (WWS2015)  ( Japan ) 2015.1

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェア工学の基礎ワークショップ(FOSE)2014  ( Japan ) 2014.12

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    Type:Competition, symposium, etc. 

  • Co-Organizers International contribution

    MSR(Mining Software Repository) Asia Summit 2014  ( Japan ) 2014.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Conference on Software Engineering Advances (ICSEA2014)  ( France ) 2014.10

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェアエンジニアリングシンポジウム2014  ( Japan ) 2014.9

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    Type:Competition, symposium, etc. 

  • 広報委員長

    ソフトウェアエンジニアリングシンポジウム2014  ( Japan ) 2014.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Conference on Software Engineering Research, Management and Applications (SERA 2014)  ( Japan ) 2014.8 - 2014.9

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    Type:Competition, symposium, etc. 

  • 座長(Chairmanship)

    第185回ソフトウェア工学研究会  ( Japan ) 2014.7

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Working Conference on Mining Software Repositories (MSR2014), Data Showcase Track  ( India ) 2014.5 - 2014.6

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    Type:Competition, symposium, etc. 

  • 座長(Chairmanship)

    第183回ソフトウェア工学研究会  ( Japan ) 2014.3

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE2014)  ( Belgium ) 2014.2

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    Type:Competition, symposium, etc. 

  • プログラム委員

    ソフトウェア工学の基礎ワークショップ(FOSE)2013  ( Japan ) 2013.11

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    Type:Competition, symposium, etc. 

  • Co-Organizers International contribution

    MSR(Mining Software Repository) Asia Summit 2013  ( Japan ) 2013.10

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Conference on Software Engineering Advances (ICSEA2013)  ( Italy ) 2013.10 - 2013.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Conference on Software Maintenanc (ICSM2013), Tool Demonstration Track  ( Netherlands ) 2013.9

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    Type:Competition, symposium, etc. 

  • 広報委員長

    ソフトウェアエンジニアリングシンポジウム2013  ( Japan ) 2013.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Working Conference on Mining Software Repositories (MSR2013), Data Challenge Track  ( UnitedStatesofAmerica ) 2013.5

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    Type:Competition, symposium, etc. 

  • Student volunteer chair and web chair International contribution

    MODULARITY: The International Conference on Aspect-Oriented Software Development (AOSD2013)  ( Japan ) 2013.3

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    Type:Competition, symposium, etc. 

  • 共同プログラム委員長

    ソフトウェア工学の基礎ワークショップ(FOSE)2012  ( Japan ) 2012.12

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    Type:Competition, symposium, etc. 

  • Program Co-chair

    JSSST Symposium on Foundations of Software Engineering  ( Japan ) 2012.12

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The Working Conference on Reverse Engineering (WCRE2012) , Tool Demonstration Track  ( Canada ) 2012.10

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    Type:Competition, symposium, etc. 

  • Tutorial Chair & Program Committee International contribution

    4th International Workshop on Empirical Software Engineering in Practice (IWESEP2012)  ( Japan ) 2012.10

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    Type:Competition, symposium, etc. 

  • プログラム委員 International contribution

    Korea-Japan Joint Workshop on ICT  ( Japan ) 2012.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Conference on Software Engineering Advances (ICSEA2012)  ( Portugal ) 2012.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    Tool Demonstration Track of The International Conference on Software Maintenanc (ICSM2012)  ( Italy ) 2012.9

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    Thematic Tracks of The International Conference on the Quality of Information and Communications Technology (QUATIC2012)  ( Portugal ) 2012.9

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    Type:Competition, symposium, etc. 

  • 座長(Chairmanship)

    日本ソフトウェア科学会第29回大会  ( Japan ) 2012.8

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    Type:Competition, symposium, etc. 

  • プログラム委員

    日本ソフトウェア科学会第29回大会  ( Japan ) 2012.8

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    The International Working Conference on Mining Software Repositories (MSR2012) Mining Challenge  ( Switzerland ) 2012.6

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    Type:Competition, symposium, etc. 

  • Vice Director

    ACM International Collegiate Programming Contest Asia Regional Contest (ACM-ICPC2011) in Fukuoka  ( Japan ) 2011.11

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    Type:Competition, symposium, etc. 

  • Program Committee International contribution

    International Workshop on Machine Learning Technologies in Software Engineering (MALETS 2011)  ( UnitedStatesofAmerica ) 2011.11

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  • 座長(Chairmanship) International contribution

    The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011)  ( Japan ) 2011.11

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    Type:Competition, symposium, etc. 

  • Workshop Chair International contribution

    The Joint Conference of the 21th International Workshop on Software Measurement (IWSM2011) and the 6th International Conference on Software Process and Product Measurement (Mensura2011)  ( Japan ) 2011.11

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    Type:Competition, symposium, etc. 

  • General Chair International contribution

    3rd International Workshop on Empirical Software Engineering in Practice (IWESEP2011)  ( Japan ) 2011.11

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    Type:Competition, symposium, etc. 

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Research Projects

  • 機械と人のインタラクションによるソフトウェア開発様式の創出

    2023.4 - 2032.3

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    Authorship:Principal investigator 

  • 研究領域「水平線の彼方の情報学」 研究課題名「機械と人のインタラクションによるソフトウェア開発様式の創出」.

    2023 - 2032

    稲盛科学研究機構(InaRIS: Inamori Research Institute for Science)フェローシップ

      More details

    Authorship:Principal investigator  Grant type:Contract research

  • プログラミング初学者の支援に向けたバグ自動修正・生成技術の創出

    Grant number:22K18630  2022 - 2024

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Challenging Research(Exploratory)

      More details

    Authorship:Principal investigator  Grant type:Scientific research funding

  • 倉田奨励金(Mobile Appコードの進化を包容するグリーンマイニング基盤の構築)

    2022

      More details

    Grant type:Donation

  • 機械がバグを修正する時代 ― 擬似オラクル生成・適用と自動バグ修正技術の深化

    2021.4 - 2025.3

      More details

    Authorship:Principal investigator 

  • 機械がバグを修正する時代―擬似オラクル生成・適用と自動バグ修正技術の深化

    Grant number:21H04877  2021 - 2024

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

      More details

    Authorship:Principal investigator  Grant type:Scientific research funding

  • SENSOR - センシブルリファクタリングの確立に向けて International coauthorship

    2020.1 - 2022.12

      More details

    Authorship:Principal investigator 

  • SENSOR - センシブルリファクタリングの確立に向けて

    2019 - 2022

    Japan Society for the Promotion of Science  国際共同研究事業スイスとの国際共同研究プログラム(JRPs)

      More details

    Authorship:Principal investigator  Grant type:Joint research

  • リポジトリマイニング分野における属人性理解に向けた研究ネットワークの構築

    2019

    Japan Society for the Promotion of Science  Bilateral program

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    Authorship:Principal investigator  Grant type:Joint research

  • 技術的負債エンジニアリング - 優先的に解決すべき技術的負債の解明とモデル化

    2018.4 - 2021.3

      More details

    Authorship:Principal investigator 

  • 自動デバッグを可能にする群衆知エコシステムの確立 研究課題

    Grant number:18H04097  2018 - 2021

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

  • 技術的負債エンジニアリング - 優先的に解決すべき技術的負債の解明とモデル化

    Grant number:18H03222  2018 - 2021

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

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    Authorship:Principal investigator  Grant type:Scientific research funding

  • 持続可能な社会を目指す次世代グリーンマイニング基盤の開発

    2018

    Japan Society for the Promotion of Science  外国人研究者招へい事業

      More details

    Authorship:Principal investigator  Grant type:Joint research

  • 日本人若手研究者研究助成金(持続可能なOSS社会の実現に向けたプロジェクト貢献情報の抽出と自動生成技術の開発)

    2018

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    Grant type:Donation

  • Mobile Appリポジトリマイニング基盤の構築とコードの自動進化に関する研究

    2016 - 2017

    Japan Society for the Promotion of Science  Postdoctoral Fellowships for Research Abroad

      More details

    Authorship:Principal investigator  Grant type:Joint research

  • Mobile Appコードの自動進化の実現に向けたリポジトリマイニング基盤の開発

    Grant number:15H05306  2015 - 2017

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (A)

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    Authorship:Principal investigator  Grant type:Scientific research funding

  • 不確かさを包容するモデル駆動開発機構に関する研究

    2014.4 - 2018.3

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    Authorship:Coinvestigator(s) 

  • 不確かさを包容するモデル駆動開発機構に関する研究

    Grant number:26240007  2014 - 2017

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (A)

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    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

  • クラッシュログからのソースコード修正箇所の推定に向けた挑戦

    Grant number:25540026  2013 - 2015

    Grants-in-Aid for Scientific Research  Grant-in-Aid for challenging Exploratory Research

      More details

    Authorship:Principal investigator  Grant type:Scientific research funding

  • リポジトリ活用型Just-In-Timeソフトウェア品質モデルの開発と評価

    2012.4 - 2015.3

      More details

    Authorship:Principal investigator 

  • リポジトリ活用型Just-In-Timeソフトウェア品質モデルの開発と評価

    Grant number:24680003  2012 - 2014

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (A)

      More details

    Authorship:Principal investigator  Grant type:Scientific research funding

  • 高信頼ソフトウェアアーキテクチャ構築に関する研究

    2011.4 - 2014.3

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    Authorship:Coinvestigator(s) 

  • Just-In-Timeバグ予測モデルの開発と適用に関する研究

    2011.4 - 2012.3

      More details

    Authorship:Principal investigator 

  • 研究領域「ポストペタスケール高性能計算に資するシステムソフトウェア技術の創出」 研究課題名「ポストペタスケール時代のスーパーコンピューティング向けソフトウェア開発環境」.

    2011 - 2016

    JST Strategic Basic Research Program (Ministry of Education, Culture, Sports, Science and Technology)

      More details

    Authorship:Coinvestigator(s)  Grant type:Contract research

  • 高信頼ソフトウェアアーキテクチャ構築に関する研究

    Grant number:23300010  2011 - 2013

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (B)

      More details

    Authorship:Coinvestigator(s)  Grant type:Scientific research funding

  • Just-In-Timeバグ予測モデルの開発と適用に関する研究

    2011

    Grants-in-Aid for Scientific Research  Grant-in-Aid for Young Scientists (Start-up)

      More details

    Authorship:Principal investigator  Grant type:Scientific research funding

▼display all

Class subject

  • プログラミング演習(P)

    2023.12 - 2024.2   Winter quarter

  • 情報理工学論議Ⅱ

    2023.10 - 2024.3   Second semester

  • 基礎PBL Ⅰ

    2023.10 - 2024.3   Second semester

  • 基礎PBL Ⅰ

    2023.10 - 2024.3   Second semester

  • システム開発演習

    2023.10 - 2024.3   Second semester

  • 情報理工学演示

    2023.10 - 2024.3   Second semester

  • 情報理工学論述Ⅱ

    2023.10 - 2024.3   Second semester

  • 【通年】情報理工学講究

    2023.4 - 2024.3   Full year

  • 国際演示技法Ⅰ

    2023.4 - 2024.3   Full year

  • 国際演示技法Ⅱ

    2023.4 - 2024.3   Full year

  • 知的財産技法Ⅰ

    2023.4 - 2024.3   Full year

  • 知的財産技法Ⅱ

    2023.4 - 2024.3   Full year

  • ティーチング演習Ⅰ

    2023.4 - 2024.3   Full year

  • ティーチング演習Ⅱ

    2023.4 - 2024.3   Full year

  • 先端プロジェクト管理技法Ⅰ

    2023.4 - 2024.3   Full year

  • 先端プロジェクト管理技法Ⅱ

    2023.4 - 2024.3   Full year

  • Scientific English Presentation I

    2023.4 - 2024.3   Full year

  • Scientific English Presentation II

    2023.4 - 2024.3   Full year

  • Intellectual Property Management I

    2023.4 - 2024.3   Full year

  • Intellectual Property Management II

    2023.4 - 2024.3   Full year

  • Exercise in Teaching I

    2023.4 - 2024.3   Full year

  • Exercise in Teaching II

    2023.4 - 2024.3   Full year

  • Advanced Project Management I

    2023.4 - 2024.3   Full year

  • Advanced Project Management II

    2023.4 - 2024.3   Full year

  • システム開発方法論特別講究

    2023.4 - 2024.3   Full year

  • Advanced Research in System Development Methodologies

    2023.4 - 2024.3   Full year

  • 情報理工学特別研究Ⅰ

    2023.4 - 2024.3   Full year

  • 情報理工学特別研究Ⅱ

    2023.4 - 2024.3   Full year

  • 情報理工学特別演習

    2023.4 - 2024.3   Full year

  • Advanced Research in Information Science and Technology I

    2023.4 - 2024.3   Full year

  • Advanced Research in Information Science and Technology II

    2023.4 - 2024.3   Full year

  • Advanced Seminar in Information Science and Technology

    2023.4 - 2024.3   Full year

  • 【通年】情報理工学研究Ⅰ

    2023.4 - 2024.3   Full year

  • 【通年】情報理工学演習

    2023.4 - 2024.3   Full year

  • 情報理工学論議Ⅰ

    2023.4 - 2023.9   First semester

  • 情報理工学読解

    2023.4 - 2023.9   First semester

  • 情報理工学論述Ⅰ

    2023.4 - 2023.9   First semester

  • 情報理工学論議Ⅱ

    2022.10 - 2023.3   Second semester

  • 基礎PBL Ⅰ

    2022.10 - 2023.3   Second semester

  • 基礎PBL Ⅰ

    2022.10 - 2023.3   Second semester

  • 情報理工学演示

    2022.10 - 2023.3   Second semester

  • 情報理工学論述Ⅱ

    2022.10 - 2023.3   Second semester

  • プログラミング演習Ⅰ(B)

    2022.6 - 2022.8   Summer quarter

  • 【サイバー】プログラム設計論特論

    2022.6 - 2022.8   Summer quarter

  • Advanced Program Design

    2022.6 - 2022.8   Summer quarter

  • [Field of Cyber]Advanced Program Design

    2022.6 - 2022.8   Summer quarter

  • Advanced Program Design

    2022.6 - 2022.8   Summer quarter

  • Advanced Seminar in Social Information Systems Engineering

    2022.4 - 2023.3   Full year

  • 国際演示技法

    2022.4 - 2023.3   Full year

  • 知的財産技法

    2022.4 - 2023.3   Full year

  • ティーチング演習

    2022.4 - 2023.3   Full year

  • 先端プロジェクト管理技法

    2022.4 - 2023.3   Full year

  • Scientific English Presentation

    2022.4 - 2023.3   Full year

  • Intellectual Property Management

    2022.4 - 2023.3   Full year

  • Exercise in Teaching

    2022.4 - 2023.3   Full year

  • Advanced Project Management Technique

    2022.4 - 2023.3   Full year

  • システム開発方法論特別講究

    2022.4 - 2023.3   Full year

  • Advanced Research in System Development Methodologies

    2022.4 - 2023.3   Full year

  • 情報知能工学特別講究第一

    2022.4 - 2023.3   Full year

  • 情報知能工学特別講究第二

    2022.4 - 2023.3   Full year

  • 知的情報システム工学特別演習

    2022.4 - 2023.3   Full year

  • 社会情報システム工学特別演習

    2022.4 - 2023.3   Full year

  • Advanced Research in Advanced Information Technology I

    2022.4 - 2023.3   Full year

  • Advanced Research in Advanced Information Technology II

    2022.4 - 2023.3   Full year

  • Adv Semi in Intelligent Information Systems Engineering

    2022.4 - 2023.3   Full year

  • 情報理工学演示

    2021.10 - 2022.3   Second semester

  • システム開発演習

    2021.10 - 2022.3   Second semester

  • 基礎PBL Ⅰ

    2021.10 - 2022.3   Second semester

  • 情報知能工学演習第三

    2021.10 - 2022.3   Second semester

  • 情報知能工学講究第三

    2021.10 - 2022.3   Second semester

  • 基礎PBL1

    2021.10 - 2022.3   Second semester

  • プログラミング演習Ⅰ(B)

    2021.6 - 2021.8   Summer quarter

  • プログラム設計論特論

    2021.6 - 2021.8   Summer quarter

  • プログラミング演習Ⅰ(B)

    2021.6 - 2021.8   Summer quarter

  • Advanced Seminar in Social Information Systems Engineering

    2021.4 - 2022.3   Full year

  • 情報理工学研究Ⅰ

    2021.4 - 2022.3   Full year

  • 情報理工学演習

    2021.4 - 2022.3   Full year

  • 国際演示技法

    2021.4 - 2022.3   Full year

  • 知的財産技法

    2021.4 - 2022.3   Full year

  • ティーチング演習

    2021.4 - 2022.3   Full year

  • 先端プロジェクト管理技法

    2021.4 - 2022.3   Full year

  • Scientific English Presentation

    2021.4 - 2022.3   Full year

  • Intellectual Property Management

    2021.4 - 2022.3   Full year

  • Exercise in Teaching

    2021.4 - 2022.3   Full year

  • Advanced Project Management Technique

    2021.4 - 2022.3   Full year

  • システム開発方法論特別講究

    2021.4 - 2022.3   Full year

  • Advanced Research in System Development Methodologies

    2021.4 - 2022.3   Full year

  • 情報知能工学特別講究第一

    2021.4 - 2022.3   Full year

  • 情報知能工学特別講究第二

    2021.4 - 2022.3   Full year

  • 知的情報システム工学特別演習

    2021.4 - 2022.3   Full year

  • 社会情報システム工学特別演習

    2021.4 - 2022.3   Full year

  • Advanced Research in Advanced Information Technology I

    2021.4 - 2022.3   Full year

  • Advanced Research in Advanced Information Technology II

    2021.4 - 2022.3   Full year

  • Adv Semi in Intelligent Information Systems Engineering

    2021.4 - 2022.3   Full year

  • プログラミング演習Ⅰ

    2021.4 - 2021.9   First semester

  • プログラミング演習(P)

    2021.4 - 2021.9   First semester

  • 情報理工学読解

    2021.4 - 2021.9   First semester

  • [M2]情報知能工学演習第二

    2021.4 - 2021.9   First semester

  • [M2]情報知能工学講究第二

    2021.4 - 2021.9   First semester

  • [M2]PBL第三

    2021.4 - 2021.9   First semester

  • プログラム設計論特論

    2021.4 - 2021.9   First semester

  • PBL III

    2021.4 - 2021.9   First semester

  • 情報理工学演習

    2021.4 - 2021.9   First semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2020.10 - 2021.3   Second semester

  • PBL II

    2020.10 - 2021.3   Second semester

  • 基礎PBL1

    2020.10 - 2021.3   Second semester

  • プログラミング演習Ⅰ(B)

    2020.6 - 2020.8   Summer quarter

  • PBL III

    2020.4 - 2020.9   First semester

  • 情報知能工学演習第二

    2020.4 - 2020.9   First semester

  • 情報知能工学講究第二

    2020.4 - 2020.9   First semester

  • PBL I

    2020.4 - 2020.9   First semester

  • 電気情報工学実験 I

    2020.4 - 2020.9   First semester

  • プログラミング演習Ⅰ

    2019.10 - 2020.3   Second semester

  • 情報知能工学演習第一

    2019.10 - 2020.3   Second semester

  • 情報知能工学演習第三

    2019.10 - 2020.3   Second semester

  • 情報知能工学講究第一

    2019.10 - 2020.3   Second semester

  • 情報知能工学講究第三

    2019.10 - 2020.3   Second semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2019.10 - 2020.3   Second semester

  • PBL II

    2019.10 - 2020.3   Second semester

  • システム開発演習

    2019.10 - 2020.3   Second semester

  • プログラミング演習Ⅰ(B)

    2019.6 - 2019.8   Summer quarter

  • PBL III

    2019.4 - 2019.9   First semester

  • PBL I

    2019.4 - 2019.9   First semester

  • 電気情報工学実験 I

    2019.4 - 2019.9   First semester

  • システム開発演習

    2018.10 - 2019.3   Second semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2018.10 - 2019.3   Second semester

  • PBL II

    2018.10 - 2019.3   Second semester

  • PBL I

    2018.4 - 2018.9   First semester

  • プログラミング演習Ⅰ

    2018.4 - 2018.9   First semester

  • PBL III

    2018.4 - 2018.9   First semester

  • 電気情報工学実験 I

    2018.4 - 2018.9   First semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2017.10 - 2018.3   Second semester

  • システム開発演習

    2017.10 - 2018.3   Second semester

  • PBL II

    2017.10 - 2018.3   Second semester

  • PBL I

    2015.4 - 2015.9   First semester

  • 数学演習II

    2015.4 - 2015.9   First semester

  • プログラミング演習

    2015.4 - 2015.9   First semester

  • プログラミング演習

    2014.10 - 2015.3   Second semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2014.10 - 2015.3   Second semester

  • 情報知能工学特別講義 (ソフトウェアリポジトリマイニング)

    2014.10 - 2015.3   Second semester

  • PBL I

    2014.4 - 2014.9   First semester

  • PBL II

    2013.10 - 2014.3   Second semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2013.10 - 2014.3   Second semester

  • 情報知能工学特別講義 (ソフトウェアリポジトリマイニング)

    2013.10 - 2014.3   Second semester

  • PBL III

    2013.4 - 2013.9   First semester

  • PBL I

    2013.4 - 2013.9   First semester

  • PBL II

    2012.10 - 2013.3   Second semester

  • 電気情報工学実験 II (ソフトウェア実験)

    2012.10 - 2013.3   Second semester

  • 情報知能工学特別講義 (ソフトウェアリポジトリマイニング)

    2012.10 - 2013.3   Second semester

  • PBL I

    2012.4 - 2012.9   First semester

  • 情報知能工学特別講義 (ソフトウェアリポジトリマイニング)

    2011.10 - 2012.3   Second semester

  • 電気情報工学実験 II (ソフトウェア実験).

    2011.10 - 2012.3   Second semester

▼display all

FD Participation

  • 2023.11   Role:Participation   Title:【シス情FD】企業等との共同研究の実施増加に向けて

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2023.9   Role:Participation   Title:【シス情FD】Top10%論文/Top10%ジャーナルとは何か: 傾向と対策

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.9   Role:Participation   Title:【シス情FD】研究機器の共用に向けて

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.6   Role:Participation   Title:【シス情FD】電子ジャーナル等の今後について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.4   Role:Participation   Title:【シス情FD】第4期中期目標・中期計画等について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2022.1   Role:Participation   Title:【シス情FD】シス情関連の科学技術に対する国の政策動向(に関する私見)

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2021.9   Role:Participation   Title:博士後期課程の充足率向上に向けて

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2020.11   Role:Participation   Title:マス・フォア・イノベーション卓越大学院について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2020.9   Role:Participation   Title:電気情報工学科総合型選抜(AO入試)について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2020.5   Role:Participation   Title:オンサイト授業 vs. オンライン授業:分かったこと,変わったこと

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2020.4   Role:Participation   Title:Moodleを利用したe-Learning実例報告(九州大学電気情報において)

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2020.4   Role:Participation   Title:新型コロナウイルスが誘起した社会変化に対する システム情報科学からの提言

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2018.9   Role:Participation   Title:教育の効率化について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2018.7   Role:Participation   Title:論文剽窃ソフトの活用方法について

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2017.10   Role:Participation   Title:いよいよスタートした電気情報工学科国際コース

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2014.8   Role:Participation   Title:新GPA制度実施のためのFD.

    Organizer:[Undergraduate school/graduate school/graduate faculty]

  • 2011.4   Role:Participation   Title:平成23年度 第1回全学FD(新任教員の研修).

    Organizer:University-wide

▼display all

Visiting, concurrent, or part-time lecturers at other universities, institutions, etc.

  • 2014  西南学院大学  Classification:Part-time lecturer  Domestic/International Classification:Japan 

  • 2013  西南学院大学  Classification:Part-time lecturer  Domestic/International Classification:Japan 

Other educational activity and Special note

  • 2023  Class Teacher  学部

  • 2018  Class Teacher  学部

Outline of Social Contribution and International Cooperation activities

  • カナダ・クイーンズ大学(Queen's University) Prof. Dr. Ahmed E. Hassanとの連携研究(2011年4月~)

Social Activities

  • ソフトウェアリポジトリマイニングの技術動向

    JEITA ソフトウェアエンジニアリング技術ワークショップ  2015.12

     More details

    Audience:General, Scientific, Company, Civic organization, Governmental agency

    Type:Seminar, workshop

Acceptance of Foreign Researchers, etc.

  • University of Waterloo

    Acceptance period: 2023.11 - 2023.12  

    Nationality:Canada

  • University of Waterloo

    Acceptance period: 2023.11  

    Nationality:Canada

  • 北アリゾナ大学

    Acceptance period: 2023.4 - 2023.9  

    Nationality:United States

  • Concordia University

    Acceptance period: 2019.6 - 2019.9  

    Nationality:Canada

  • Queen's University

    Acceptance period: 2019.2 - 2019.5  

    Nationality:Canada

  • University of Wollongong

    Acceptance period: 2018.8 - 2018.9  

    Nationality:Australia

  • McGill University

    Acceptance period: 2018.7 - 2018.8  

    Nationality:Canada

  • University of Alberta

    Acceptance period: 2018.4 - 2018.7  

    Nationality:Canada

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Travel Abroad

  • 2015.8 - 2017.7

    Staying countory name 1:Canada   Staying institution name 1:Queen's University