九州大学 研究者情報
論文一覧
亀井 靖高(かめい やすたか) データ更新日:2021.05.25

准教授 /  システム情報科学研究院 情報知能工学部門 高度ソフトウェア工学


原著論文
1. Gopi Krishnan Rajbahadur, Shaowei Wang, Gustavo Ansaldi, Yasutaka Kamei, Ahmed E. Hassan, The impact of feature importance methods on the interpretation of defect classifiers, IEEE Transactions on Software Engineering, 10.1109/TSE.2021.3056941, 2021.02, [URL], 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..
2. Jeongju Sohn, Yasutaka Kamei, Shane McIntosh, Shin Yoo, Leveraging Fault Localisation to Enhance Defect Prediction. , 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), 2021.03.
3. Ryujiro Nishinaka, Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato, How Fast and Effectively Can Code Change History Enrich Stack Overflow? , Proceedings - IEEE International Conference on Software Quality, Reliability and Security, QRS 2020, 10.1109/QRS51102.2020.00066, 467-478, 2020.12, [URL].
4. Yasutaka Kamei, Andy Zaidman, Guest editorial
Mining software repositories 2018, Empirical Software Engineering, 10.1007/s10664-020-09817-8, 25, 3, 2055-2057, 2020.05, [URL].
5. 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, 情報処理学会論文誌, 2020.04.
6. Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato, When and Why Do Software Developers Face Uncertainty?, 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
, 10.1109/QRS.2019.00045, 288-299, 2019.07, [URL], 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..
7. Giancarlo Sierra, Emad Shihab, Yasutaka Kamei, A survey of self-admitted technical debt, Journal of Systems and Software, 10.1016/j.jss.2019.02.056, 152, 70-82, 2019.06, [URL], 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..
8. Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi, DeepJIT: An end-to-end deep learning framework for just-in-time defect prediction, IEEE International Working Conference on Mining Software Repositories, 10.1109/MSR.2019.00016, 2019-May, 34-45, 2019.05, © 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)..
9. Naoyasu Ubayashi, Takuya Watanabe, Yasutaka Kamei, Ryosuke Sato, Git-based integrated uncertainty manager, 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
, 10.1109/ICSE-Companion.2019.00047, 95-98, 2019.05, [URL], 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/..
10. Shaiful Alam Chowdhury, Abram Hindle, Rick Kazman, Takumi Shuto, Ken Matsui, Yasutaka Kamei, GreenBundle: An Empirical Study on the Energy Impact of Bundled Processing, Proceedings - International Conference on Software Engineering, 10.1109/ICSE.2019.00114, 2019-May, 1107-1118, 2019.05, © 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%..
11. Hoa Khanh Dam, Truyen Tran, John Grundy, Aditya Ghose, Yasutaka Kamei, Towards effective AI-powered agile project management, 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
, 10.1109/ICSE-NIER.2019.00019, 41-44, 2019.05, [URL], 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..
12. Naoyasu Ubayashi, Yasutaka Kamei, Ryosuke Sato, IARCH-U/MC
An uncertainty-aware model checker for embracing known unknowns, 13th International Conference on Software Technologies, ICSOFT 2018
ICSOFT 2018 - Proceedings of the 13th International Conference on Software Technologies
, 176-184, 2019.01, 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..
13. Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan, Impact of Discretization Noise of the Dependent variable on Machine Learning Classifiers in Software Engineering, IEEE Transactions on Software Engineering, 10.1109/TSE.2019.2924371, 2019.01, [URL], 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..
14. Masanari Kondo, Cor Paul Bezemer, Yasutaka Kamei, Ahmed E. Hassan, Osamu Mizuno, The impact of feature reduction techniques on defect prediction models, Empirical Software Engineering, 10.1007/s10664-018-9679-5, 2019.01, [URL], 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..
15. Yasutaka Kamei, Takahiro Matsumoto, Kazuhiro Yamashita, Naoyasu Ubayashi, Takashi Iwasaki, Shuichi Takayama, Studying the Cost and Effectiveness of OSS Quality Assessment Models: An Experience Report of Fujitsu QNET, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 10.1587/transinf.2018EDP7163, E101D, 11, 2744-2753, 2018.11, 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..
16. Junji Shimagaki, Yasutaka Kamei, Naoyasu Ubayashi, Abram Hindle, Automatic topic classification of test cases using text mining at an Android smartphone vendor, 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
, 10.1145/3239235.3268927, 2018.10, [URL], 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..
17. Takashi Watanabe, Akito Monden, Zeynep Yucel, Yasutaka Kamei, Shuji Morisaki, Cross-Validation-Based Association Rule Prioritization Metric for Software Defect Characterization, IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 10.1587/transinf.2018EDP7020, E101D, 9, 2269-2278, 2018.09, 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..
18. Shane McIntosh, Yasutaka Kamei, Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction, IEEE Transactions on Software Engineering, 10.1109/TSE.2017.2693980, 44, 5, 412-428, 2018.05, [URL], 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..
19. Ariel Rodriguez, Fumiya Tanaka, Yasutaka Kamei, Empirical study on the relationship between developer's working habits and efficiency, 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
, 10.1145/3196398.3196458, 74-77, 2018.05, [URL], 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..
20. Naoyasu Ubayashi, Hokuto Muraoka, Daiki Muramoto, Yasutaka Kamei, Ryosuke Sato, Poster: Exploring uncertainty in GitHub OSS projects: When and how do developers face uncertainty?, Proceedings - International Conference on Software Engineering, 10.1145/3183440.3194966, 272-273, 2018.05, © 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..
21. 中野 大扉, 亀井 靖高, 佐藤 亮介, 鵜林 尚靖, 高山 修一, 岩崎 孝司, OSS事前品質評価における重み付け手法の実証実験, コンピュータソフトウェア, 2018.03.
22. 廣瀬 賢幸, 鵜林 尚靖, 亀井 靖高, 佐藤 亮介, Stack Overflowを利用した自動バグ修正の検討, コンピュータソフトウェア, 2018.03.
23. Xiaochen Li, He Jiang, Yasutaka Kamei, Xin Chen, Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding, IEEE Transactions on Software Engineering, 10.1109/TSE.2018.2876006, 2018.01, [URL], 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..
24. Keisuke Watanabe, Naoyasu Ubayashi, Takuya Fukamachi, Shunya Nakamura, Hokuto Muraoka, Yasutaka Kamei, iArch-U: Interface-Centric Integrated Uncertainty-aware Development Environment, International Workshop on Modeling in Software Engineering (MiSE2017), 2017.05.
25. Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei, Ahmed E. Hassan, The Impact Of Using Regression Models to Build Defect Classifiers, International Conference on Mining Software Repositories (MSR 2017), 2017.05.
26. Keisuke Watanabe, Takuya Fukamachi, Naoyasu Ubayashi, Yasutaka Kamei, Automated A/B Testing with Declarative Variability Expressions, Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017, 10.1109/ICSTW.2017.72, 387-388, 2017.04, © 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..
27. Ayse Tosun, Emad Shihab, Yasutaka Kamei, Erratum to: Studying high impact fix-inducing changes (Empirical Software Engineering, (2016), 21, 2, (605-641), 10.1007/s10664-015-9370-z), Empirical Software Engineering, 10.1007/s10664-016-9455-3, 22, 2, 848, 2017.04, © 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..
28. Pawin Suthipornopas, Pattara Leelaprute, Akito Monden, Hidetake Uwano, Yasutaka Kamei, Naoyasu Ubayashi, Kenji Araki, Kingo Yamada, Ken-ichi Matsumoto, Industry Application of Software Development Task Measurement System : TaskPit, IEICE Transactions on Information and Systems, Vol.E100-D, No.3, pp.(To Appear), 2017.03.
29. 戸田 航史, 亀井 靖高, 吉田 則裕,, 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, 情報処理学会論文誌, 2017.03.
30. 渡辺 啓介, 深町 拓也, 鵜林 尚靖, 細合 晋太郎, 亀井 靖高, 宣言的な可変性記述によるA/Bテストの自動化, コンピュータソフトウェア, 2017.02.
31. Yasutaka Kamei, Everton Maldonado, Emad Shihab, Naoyasu Ubayashi, Using Analytics to Quantify the Interest of Self-Admitted Technical Debt, International Workshop on Technical Debt Analytics (TDA2016), pp.1-4, December 2016., 2016.12.
32. Junji Shimagaki, Yasutaka Kamei, Shane Mcintosh, David Pursehouse and Naoyasu Ubayashi, Why are Commits being Reverted? A Comparative Study of Industrial and Open Source Projects, International Conference on Software Maintenance and Evolution (ICSME2016), pp.301-311, October 2016. (Raleigh, North Carolina, USA), 2016.10, 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..
33. Xin Xia, Emad Shihab, Yasutaka Kamei, David Lo and Xinyu Wang, Predicting Crashing Releases of Mobile Applications, International Symposium on Empirical Software Engineering and Measurement (ESEM), (To appear). (Ciudad Real, Spain)., pp.29:1-29:10, September 2016. (Ciudad Real, Spain)., 2016.09, 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..
34. Keisuke Miura, Shane Mcintosh, Yasutaka Kamei, Ahmed E. Hassan and Naoyasu Ubayashi, The Impact of Task Granularity on Co-evolution Analyses, International Symposium on Empirical Software Engineering and Measurement (ESEM), (To appear). (Ciudad Real, Spain)., 2016.09, 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..
35. Kwabena Ebo Bennin, Koji Toda, Yasutaka Kamei, Jacky Keung, Akito Monden and Naoyasu Ubayashi, Empirical Evaluation of Cross-Release Effort-Aware Defect Prediction Models, International Conference on Software Quality, Reliability and Security (QRS2016), pp.214-221, August 2016. (Vienna, Austria)., 2016.08.
36. Kazuhiro Yamashita, Changyun Huang, Meiyappan Nagappan, Yasutaka Kamei, Audris Mockus, Ahmed E. Hassan and Naoyasu Ubayashi, Thresholds for Size and Complexity Metrics: A Case Study from the Perspective of Defect Density, International Conference on Software Quality, Reliability and Security (QRS2016), pp.191-201, August 2016. (Vienna, Austria)., 2016.08.
37. Takashi Watanabe, Akito Monden, Yasutaka Kamei, Shuji Morisaki, Identifying Recurring Association Rules in Software Defect Prediction, International Conference on Computer and Information Science (ICIS2016), pp.1-6, June 2016. (Okayama, Japan)., 2016.06.
38. Kwabena Ebo Bennin, Jacky Keung, Akito Monden, Yasutaka Kamei and Naoyasu Ubayashi, Investigating the Effects of Balanced Training and Testing Data Sets on Effort-Aware Fault Prediction Models, International Conference on Computers, Software and Applications (COMPSAC), 2016.06.
39. Junji Shimagaki, Yasutaka Kamei, Shane Mcintosh, Ahmed E. Hassan and Naoyasu Ubayashi, A Study of the Quality-Impacting Practices of Modern Code Review at Sony Mobile, the International Conference on Software Engineering (ICSE2016) Software Engineering in Practice (SEIP), 2016.05, 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..
40. Masateru Tsunoda, Yasutaka Kamei, Atsushi Sawada, Assessing the differences of clone detection methods used in the fault-prone module prediction, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering, SANER 2016, 10.1109/SANER.2016.65, 15-16, 2016.05, © 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..
41. Bodin Chinthanet, Passakorn Phannachitta, Yasutaka Kamei, Pattara Leelaprute, Arnon Rungsawang, Naoyasu Ubayashi and Kenichi Matsumoto, A Review and Comparison of Methods for Determining the Best Analogies in Analogy-based Software Effort Estimation, International Symposium on Applied Computing (SAC 2016) Poster Session, 2016.04.
42. Yasutaka Kamei, Emad Shihab, Defect Prediction: Accomplishments and Future Challenges, Leaders of Tomorrow / Future of Software Engineering Track at International Conference on Software Analysis Evolution and Reengineering (SANER2016), Issue 2, pp.99-104., 2016.03.
43. Felienne Hermans, Janet Siegmund, Thomas Fritz, Gabriele Bavota, Meiyappan Nagappan, Abram Hindle, Yasutaka Kamei, Ali Mesbah, Bram Adams, Leaders of tomorrow on the future of software engineering: A roundtable, IEEE Software, 10.1109/MS.2016.55, 33, 2, 99-104, 2016.03, © 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..
44. Kazuhiro Yamashita, Yasutaka Kamei, Shane McIntosh, Ahmed E. Hassan and Naoyasu Ubayashi, Magnet or Sticky? Measuring Project Characteristics from the Perspective of Developer Attraction and Retention, Journal of Information Processing, Vol.24, No.2, pp.339-348, 2016.03.
45. Yasutaka Kamei, Software Quality Assurance 2.0: Proactive, Practical, and Relevant, IEEE SOFTWARE, 33, 2, 102-103, 2016.03.
46. 小須田 光, 亀井 靖高, 鵜林 尚靖, 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, コンピュータソフトウェア, Vol.32, No.4, pp.131-140,, 2015.12.
47. 中川 尊雄, 亀井 靖高, 上野 秀剛, 門田 暁人, 鵜林 尚靖, 松本 健一, 脳活動に基づくプログラム理解の困難さ測定, コンピュータソフトウェア, 2015.11.
48. Meiyappan Nagappan, Romain Robbes, Yasutaka Kamei, Eric Tanter, Shane Mcintosh, Audris Mockus, Ahmed E. Hassan, An Empirical Study of goto in C Code from GitHub Repositories, the ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE2015), pp.404-414, 2015.09.
49. Yasutaka Kamei, Takafumi Fukushima, Shane McIntosh, Kazuhiro Yamashita, Naoyasu Ubayashi and Ahmed E. Hassan, Studying Just-In-Time Defect Prediction using Cross-Project Models, Journal of Empirical Software Engineering, Online first (pp.1-35), 2015.09.
50. Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei, Ahmed E. Hassan and Naoyasu Ubayashi, Revisiting the Applicability of the Pareto Principle to Core Development Teams in Open Source Software Projects, International Workshop on Principles of Software Evolution (IWPSE 2015), pp.46-55, 2015.08.
51. Shane Mcintosh, Yasutaka Kamei, Bram Adams and Ahmed E. Hassan, An Empirical Study of the Impact of Modern Code Review Practices on Software Quality, Journal of Empirical Software Engineering, Online first (pp.1-45), 2015.05.
52. Takuya Fukamachi, Naoyasu Ubayashi, Shintaro Hosoai, Yasutaka Kamei, Modularity for Uncertainty, International Workshop on Modeling in Software Engineering (MiSE2015), pp.7-12, 2015.05.
53. Takuya Fukamachi, Naoyasu Ubayashi, Shintaro Hosoai, Yasutaka Kamei, Poster: Conquering Uncertainty in Java Programming, International Conference on Software Engineering (ICSE2015), Poster Session., 2015.05.
54. Ayse Tosun Misirli, Emad Shihab, Yasutaka Kamei, Studying High Impact Fix-Inducing Changes, Journal of Empirical Software Engineering, Online first (pp.1-37), 2015.05.
55. Changyun Huang, Ataru Osaka, Yasutaka Kamei, Naoyasu Ubayashi, Automated DSL Construction Based on Software Product Lines, International Conference on Model-Driven Engineering and Software Development (MODELSWARD2015), Poster Session, 2015.02.
56. 戸田 航史, 亀井 靖高, 濵﨑 一樹, 吉田 則裕, 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, コンピュータソフトウェア, Vol.32, No.1, pp.227-233, 2015.02.
57. 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, Vol.56, No.2, pp,669-681, 2015.02.
58. Peiyuan Li, Naoyasu Ubayashi, Di Ai, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei, Sketch-Based Gradual Model-Driven Development, International Workshop on Innovative Software Development Methodologies and Practices (InnoSWDev 2014), pp.100-105, 2014.11.
59. Naoyasu Ubayashi, Di Ai, Peiyuan Li, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei, Uncertainty-Aware Architectural Interface, International Workshop on Advanced Modularization Techniques (AOAsia/Pacific 2014), pp.4-6, 2014.11.
60. Akinori Ihara, Yasutaka Kamei, Masao Ohira, Ahmed E. Hassan, Naoyasu Ubayashi and Kenichi Matsumoto, Early Identification of Future Committers in Open Source Software Projects, International Conference on Quality Software (QSIC2014), pp.47-56, 2014.10.
61. Naoyasu Ubayashi, Di Ai, Peiyuan Li, Yu Ning Li, Shintaro Hosoai and Yasutaka Kamei, Abstraction-aware Verifying Compiler for Yet Another MDD, International Conference on Automated Software Engineering (ASE 2014) [new ideas paper track], pp.557-562, 2014.09.
62. 中川 尊雄, 亀井 靖高, 上野 秀剛, 門田 暁人, 松本 健一, プログラム理解の困難さの脳血流による計測の試み, コンピュータソフトウェア, Vol.31, No.3, pp.270-276, 2014.08.
63. Shuhei Ohsako, Yasutaka Kamei, Shintaro Hosoai, Weiqiang Kong, Kimitaka Kato, Akihiko Ishizuka, Kazutoshi Sakaguchi, Miyuki Kawataka, Yoshitsugu Morita, Naoyasu Ubayashi and Akira Fukuda, A Case Study on Introducing the Design Thinking into PBL, International Conference on Frontiers in Education: CS and CE (FECS 2014), 2014.07.
64. Takafumi Fukushima, Yasutaka Kamei, Shane McIntosh, Kazuhiro Yamashita and Naoyasu Ubayashi, An Empirical Study of Just-In-Time Defect Prediction Using Cross-Project Models, International Working Conference on Mining Software Repositories (MSR 2014), pp.172-181, 2014.06.
65. Kazuhiro Yamashita, Shane McIntosh, Yasutaka Kamei and Naoyasu Ubayashi, Magnet or Sticky?: An OSS Project-by-Project Typology, International Working Conference on Mining Software Repositories (MSR 2014), pp.344-347, 2014.06.
66. Takao Nakagawa, Yasutaka Kamei, Hidetake Uwano, Akito Monden, Kenichi Matsumoto and Daniel M. German, Quantifying Programmers' Mental Workload during Program Comprehension Based on Cerebral Blood Flow Measurement: A Controlled Experiment, International Conference on Software Engineering (ICSE2014), NIER Track, pp.448-451, 2014.06.
67. 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), pp.192-201, 2014.06.
68. Daisuke Nakano, Akito Monden, Yasutaka Kamei, Kenichi Matsumoto, Simulation of effort allocation strategies in software testing using bug module, Computer Software, 31, 2, 118-128, 2014.05, 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..
69. 中野 大輔, 門田 暁人, 亀井 靖高, 松本 健一, 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, コンピュータソフトウェア, Vol.31, No.2, pp.118-128, 2014.05.
70. 角田 雅照, 戸田 航史, 伏田 享平, 亀井 靖高, Meiyappan Nagappan, 鵜林 尚靖, 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, コンピュータソフトウェア, Vol.31, No.2, pp.129-143, 2014.05.
71. Di Ai, Naoyasu Ubayashi, Peiyuan Li, Daisuke Yamamoto, Yu Ning Li, Shintaro Hosoai, Yasutaka Kamei, iArch: An IDE for Supporting Fluid Abstraction, International Conference on Modularity'14, Tool Demo Session, 2014.04.
72. Changyun Huang, Naoyasu Ubayashi and Yasutaka Kamei, Towards Language-Oriented Software Development, International Workshop on Open and Original Problems in Software Language Engineering (OOPSLE 2014), 2014.02.
73. Di Ai, Naoyasu Ubayashi, Peiyuan Li, Shintaro Hosoai and Yasutaka Kamei, iArch - An IDE for Supporting Abstraction-aware Design Traceability, International Conference on Model-Driven Engineering and Software Development (MODELSWARD2014), Poster Session, 2014.01.
74. Emad Shihab, Yasutaka Kamei, Bram Adams, and Ahmed E. Hassan, Is Lines of Code a Good Measure of Effort in Effort-Aware Models?, Information and Software Technology, Vol.55, No.11, 2013.11.
75. Emad Shihab, Akinori Ihara, Yasutaka Kamei, Walid M. Ibrahim, Masao Ohira, Bram Adams, Ahmed E. Hassan and Ken-ichi Matsumoto, Studying Re-opened Bugs in Open Source Software, Journal of Empirical Software Engineering, Vol.18, No.5, pp.1005-1042, 2013.10.
76. 小林 寛武, 戸田 航史, 亀井 靖高, 門田 暁人, 峯 恒憲, 鵜林 尚靖, 11種類のfault密度予測モデルの実証的評価, 電子情報通信学会論文誌, Vol.J96-D, No.8, pp.1892-1902, 2013.08.
77. Changyun Huang, Yasutaka Kamei, Kazuhiro Yamashita and Naoyasu Ubayashi, Using Alloy to Support Feature-Based DSL Construction for Mining Software Repositories, International Workshop on Model-driven Approaches in Software Product Line Engineering and Workshop on Scalable Modeling Techniques for Software Product Lines (MAPLE/SCALE 2013), 2013.08.
78. Masateru Tsunoda, Kyohei Fushida, Yasutaka Kamei, Masahide Nakamura, Kohei Mitsui, Keita Goto, and Ken-ichi Matsumoto, An Authentication Method with Spatiotemporal Interval and Partial Matching, International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2013), 2013.07.
79. Tetsuya Oishi, Weiqiang Kong, Yasutaka Kamei, Norimichi Hiroshige, Naoyasu Ubayashi and Akira Fukuda, An Empirical Study on Remote Lectures Using Video Conferencing Systems, International Conference on Frontiers in Education: CS and CE (FECS 2013), 2013.07.
80. 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, 2013.06.
81. Naoyasu Ubayashi and Yasutaka Kamei, Design Module: A Modularity Vision Beyond Code -Not Only Program Code But Also a Design Model Is a Module-, International Workshop on Modeling in Software Engineering (MiSE2013), 2013.05.
82. Changyun Huang, Kazuhiro Yamashita, Yasutaka Kamei, Kenji Hisazumi and Naoyasu Ubayashi, Domain Analysis for Mining Software Repositories -Towards Feature-based DSL Construction-, International Workshop on Product LinE Approaches in Software Engineering (PLEASE 2013), 2013.05.
83. Masateru Tsunoda, Koji Toda, Kyohei Fushida, Yasutaka Kamei, Meiyappan Nagappan and Naoyasu Ubayashi, Revisiting Software Development Effort Estimation Based on Early Phase Development Activities, International Working Conference on Mining Software Repositories (MSR 2013), 2013.05.
84. Tetsuya Oishi, Yasutaka Kamei, Weiqiang Kong, Norimichi Hiroshige, Naoyasu Ubayashi, Akira Fukuda, An Experience Report on Remote Lecture Using Multi-point Control Unit, International Conference on Education and Teaching (ICET 2013), pp.1-8, 2013.03.
85. Naoyasu Ubayashi and Yasutaka Kamei, UML-based Design and Verification Method for Developing Dependable Context-Aware Systems, International Conference on Model-Driven Engineering and Software Development (MODELSWARD 2013), pp.89-94, 2013.02.
86. Akito Monden, Jacky Keung, Shuji Morisaki, Yasutaka Kamei and Kenichi Matsumoto, A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction, Asia-Pacific Software Engineering Conference (APSEC 2012), pp.838-847, 2012.12.
87. Akinori Ihara, Yasutaka Kamei, Akito Monden, Masao Ohira, Jacky Keung, Naoyasu Ubayashi and Kenichi Matsumoto, An Investigation on Software Bug Fix Prediction for Open Source Software Projects -A Case Study on the Eclipse Project-, International Workshop on Software Analysis, Testing and Applications (SATA2012), pp.112-119, 2012.12.
88. Phiradet Bangcharoensap, Akinori Ihara, Yasutaka Kamei, Ken-ichi Matsumoto, Locating Source Code to be Fixed based on Initial Bug Reports -A Case Study on the Eclipse Project, International Workshop on Empirical Software Engineering in Practice (IWESEP2012), pp.10-15, 2012.10.
89. Hiroki Nakamura, Rina Nagano, Kenji Hisazumi, Yasutaka Kamei, Naoyasu Ubayashi and Akira Fukuda, QORAL : External Domain-Specific Language for Mining Software Repositories., International Workshop on Empirical Software Engineering in Practice (IWESEP2012), pp.23-29, 2012.10.
90. Naoyasu Ubayashi and Yasutaka Kamei, UML4COP: UML-based DSML for Context-Aware Systems, International Workshop on Domain-Specific Modeling (DSM2012), pp.33-38, 2012.10.
91. 内尾 静, 鵜林 尚靖, 亀井 靖高, 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, コンピュータソフトウェア, Vol.29, No.3, pp.108-114, 2012.08.
92. Rina Nagano, Hiroki Nakamura, Yasutaka Kamei, Bram Adams, Kenji Hisazumi, Naoyasu Ubayashi and Akira Fukuda, Using the GPGPU for Scaling Up Mining Software Repositories, International Conference on Software Engineering (ICSE2012), Poster Session, pp.1435-1436, 2012.06.
93. Naoyasu Ubayashi, Yasutaka Kamei, Verifiable Architectural Interface for Supporting Model-Driven Development with Adequate Abstraction Level, International Workshop on Modeling in Software Engineering (MiSE2012), pp.15-21, 2012.06.
94. Naoyasu Ubayashi, Yasutaka Kamei, An Extensible Aspect-oriented Modeling Environment for Constructing Domain-Specific Languages, IEICE Transactions on Information and Systems, Vol.E95-D No.4 pp.942-958., 2012.04.
95. Naoyasu Ubayashi and Yasutaka Kamei, Architectural Point Mapping for Design Traceability, Foundations of Aspect-Oriented Languages workshop (FOAL2012), pp.39-44, 2012.03.
96. 塩塚 大, 鵜林 尚靖, 亀井 靖高, dcNavi: デバッグを支援する関心事指向推薦システム, 情報処理学会論文誌, Vol.53, No.2, pp.631-643., 2012.03.
97. 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, 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, 情報社会学会学会誌, Vol.6, No.2, pp.17-32., 2012.03.
98. 藏本 達也, 亀井 靖高, 門田 暁人, 松本 健一, 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, 電子情報通信学会論文誌, Vol.J95-D, No.3, pp.425-436., 2012.03.
99. 伊原 彰紀, 亀井 靖高, 大平 雅雄, 松本 健一, 鵜林 尚靖, 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, 電子情報通信学会論文誌, Vol.J95-D, No.2, pp.237-249., 2012.02.
100. 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, 時空間情報と動作に基づく認証方法, 知能と情報(日本知能情報ファジィ学会誌), Vol.23, No.6, pp.874-881., 2011.12.
101. Hidetake Uwano, Yasutaka Kamei, Akito Monden, Ken-Ichi Matsumoto, An Analysis of Cost-overrun Projects using Financial Data and Software Metrics, The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011), pp.227-232, 2011.11.
102. Yasutaka Kamei, Hiroki Sato, Akito Monden, Shinji Kawaguchi, Hidetake Uwano, Masataka Nagura, Ken-Ichi Matsumoto, Naoyasu Ubayashi, An Empirical Study of Fault Prediction with Code Clone Metrics, The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011), pp.55-61, 2011.11.
103. Ryosuke Nakashiro, Yasutaka Kamei, Naoyasu Ubayashi, Shin Nakajima, Akihito Iwai, Translation Pattern of BPEL Process into Promela Code, The Joint Conference of the 21th International Workshop on Software Measurement and the 6th International Conference on Software Process and Product Measurement (IWSM/MENSURA2011), pp.285-290, 2011.11.
104. Emad Shihab, Audris Mockus, Yasutaka Kamei, Bram Adams, Ahmed E. Hassan,, High-Impact Defects: A Study of Breakage and Surprise Defects, the ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE2011), pp.300-310, 2011.09.
105. Naoyasu Ubayashi, Yasutaka Kamei, Masayuki Hirayama, Tetsuo Tamai, Context Analysis Method for Embedded Systems ---Exploring a Requirement Boundary between a System and Its Context, 3rd Workshop on Context-Oriented Programming (COP 2011), pp.143-152, 2011.08.
106. Shuji Morisaki, Yasutaka Kamei, and Ken-ichi Matsumoto, Experimental Evaluation of Effect of Specifying a Focused Defect Classification in Software Inspection, JSSST Journal, Vol.28, No.3, pp.173-178, 2011.08.
107. Shizuka Uchio, Naoyasu Ubayashi, Yasutaka Kamei, CJAdviser: SMT-based Debugging Support for ContextJ*, 3rd Workshop on Context-Oriented Programming (COP 2011), pp.1-6, 2011.07.
108. Masaru Shiozuka, Naoyasu Ubayashi, Yasutaka Kamei, Debug Concern Navigator, the 23rd International Conference on Software Engineering and Knowledge Engineering (SEKE 2011), pp.197-202, 2011.07.
109. Naoyasu Ubayashi, Yasutaka Kamei, Stepwise Context Boundary Exploration Using Guide Words, the 23rd International Conference on Advanced Information Systems Engineering (CAiSE 2011 Forum), pp.131-138., 2011.06.
110. Shane McIntosh, Bram Adams, Thanh H. D. Nguyen, Yasutaka Kamei and Ahmed E. Hassan, An Empirical Study of Build Maintenance Effort, the 33rd International Conference on Software Engineering (ICSE2011), pp.141-150, 2011.05.
111. [マツ]本 真佑, 亀井 靖高, 門田 暁人, 松本 健一, 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, 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, 2010.08, ソフトウェアの信頼性に影響を及ぼす要因として,ソフトウェアプロダクトの特徴から算出されたメトリックスを用いた信頼性の分析が数多く行われている.本論文ではプロダクトそのものの特性ではなく,プロダクトを作成した開発者の特性(開発者メトリックス)に基づいたソフトウェア信頼性の分析を行う.用いる開発者メトリックスは開発者ごとの変更行数やコミット回数などと,モジュールごとの開発にかかわった開発者の数などである.本論文の分析は以下の四つの仮説に基づく.仮説1a:バグの混入のさせやすさには個人差がある.仮説1b:バグの混入のさせやすさは開発者の特性(変更行数やコミット回数など)から判断できる.仮説2:多くの開発者が変更したモジュールにはバグが混入されやすい.仮説3:開発者メトリックスはfault-proneモジュールの判別に役立つ.Eclipseプロジェクトから収集したメトリックスデータを用いた分析の結果,すべての仮説が支持され,開発者のバグの混入のさせやすさには少なくとも5倍以上の個人差があること,及び多くの開発者が関与したモジュールほど,わずかではあるがバグが混入されやすいことが明らかとなった..
112. 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, 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, 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, 2010.04, 本論文では,馬場らによる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を行った.Eclipseプロジェクトより収集した3バージョン分(バージョン3.0,3.1,3.2)のモジュールデータを用いた実験の結果,先行研究とは異なり精度の向上は確認できなかった.本論文では,精度が向上しなかった要因を調べるためにクローンメトリックスとfaultの関係を分析した.分析結果から,クローンメトリックスは規模の小さいモジュールに対するfault-proneモジュール判別には効果がないものの,馬場らが対象とするようなある程度規模の大きいモジュールに対しては効果があることが示唆された..
113. 柿元 健, 門田 暁人, 亀井 靖高, 柗本 真佑, 松本 健一, 楠本 真二, 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, 情報処理学会論文誌, 50, 7, 1716-1724, 2009.07, ソフトウェアの信頼性確保を目的として,faultの有無を推定するモデル(faultproneモジュール判別モデル)が数多く提案されている.しかし,どのようにテスト工数を割り当てるのかといったfault-proneモジュール判別モデルの判別結果の利用方法についての議論はほとんどされておらず,信頼性確保の効果は不明確であった.そこで,本論文では,faultの有無の判別,テスト工数の割当て,ソフトウェア信頼性の関係のモデル化を行い,TEAR(Test Effort Allocation and software Reliability)モデルを提案する.TEARモデルにより,与えられた総テスト工数の枠内で,ソフトウェア信頼性が最大となるような(モジュールごとの)テスト工数割当ての計画立案が可能となる.TEARモデルを用いてシミュレーションを行った結果,推定される判別精度が高い,もしくは,fault含有モジュールが少ない場合には,fault-proneモジュールに多くのテスト工数を割り当てた方がよく,推定される判別精度が低い,もしくは,fault含有モジュールを多く含む場合には,判別結果に基づいてテスト工数を割り当てるべきではないことが分かった.; 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..

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