記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/3510003.3510116

その他リンク： https://arxiv.org/pdf/2202.05751

担当区分：筆頭著者   記述言語：英語  

出版者・発行元：Proceedings Asia Pacific Software Engineering Conference APSEC  

Technical debt refers to suboptimal code that degrades software quality. When developers intentionally introduce such debt, it is called self-admitted technical debt (SATD). Since SATD hinders maintenance, identifying its categories is key to uncovering quality issues. Traditionally, constructing such taxonomies requires manually inspecting SATD comments and surrounding code, which is time-consuming, labor-intensive, and often inconsistent due to annotator subjectivity. In this study, we investigate to what extent large language models (LLMs) can generate SATD taxonomies. We designed a structured, LLM-driven pipeline that mirrors the taxonomy construction steps researchers typically follow. We evaluated it on SATD datasets from three domains: quantum software, smart contracts, and machine learning. It successfully recovered domain-specific categories reported in prior work, such as Layer Configuration in machine learning. It also completed taxonomy generation in under two hours and for less than $1, even on the largest dataset. These results suggest that, while full automation remains challenging, LLMs can support semi-automated SATD taxonomy construction. Furthermore, our work opens up avenues for future work, such as automated taxonomy generation in other areas.

DOI： 10.1109/APSEC66846.2025.00087

Web of Science

Scopus

出版者・発行元：Proceedings Asia Pacific Software Engineering Conference APSEC  

Code review is a key practice in software engineering, where developers evaluate code changes to ensure quality and maintainability. Links to issues and external resources are often included in Pull Requests (PRs) to provide additional context, yet they are typically discarded in automated tasks such as PR summarization and code review comment generation. This limits the richness of information available to reviewers and increases cognitive load by forcing context-switching. To address this gap, we present AILINKPREVIEWER, a tool that leverages Large Language Models (LLMs) to generate previews of links in PRs using PR metadata, including titles, descriptions, comments, and link body content. We analyzed 50 engineered GitHub repositories and compared three approaches: Contextual LLM summaries, Non-Contextual LLM summaries, and Metadata-based previews. The results in metrics such as BLEU, BERTScore, and compression ratio show that contextual summaries consistently outperform other methods. However, in a user study with seven participants, most preferred non-contextual summaries, suggesting a trade-off between metric performance and perceived usability. These findings demonstrate the potential of LLM-powered link previews to enhance code review efficiency and to provide richer context for developers and automation in software engineering. The video demo is available at https://www.youtube.com/ watch?v=h2qH4RtrB3E, and the tool and its source code can be found at https://github.com/c4rtune/AILinkPreviewer.

DOI： 10.1109/APSEC66846.2025.00121

Web of Science

Scopus

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/3643991.3644914

その他リンク： https://rebels.cs.uwaterloo.ca/papers/msr2024_zeng.pdf

記述言語：英語   掲載種別：研究論文（国際会議プロシーディングス）  

DOI： 10.1145/3597503.3639127

その他リンク： https://arxiv.org/pdf/2401.02755

出版者・発行元：Empirical Software Engineering  

Open source software development has become more social and collaborative, evident GitHub. Since 2016, GitHub started to support more informal methods such as emoji reactions, with the goal to reduce commenting noise when reviewing any code changes to a repository. From a code review context, the extent to which emoji reactions facilitate a more efficient review process is unknown. We conduct an empirical study to mine 1,850 active repositories across seven popular languages to analyze 365,811 Pull Requests (PRs) for their emoji reactions against the review time, first-time contributors, comment intentions, and the consistency of the sentiments. Answering these four research perspectives, we first find that the number of emoji reactions has a significant correlation with the review time. Second, our results show that a PR submitted by a first-time contributor is less likely to receive emoji reactions. Third, the results reveal that the comments with an intention of information giving, are more likely to receive an emoji reaction. Fourth, we observe that only a small proportion of sentiments are not consistent between comments and emoji reactions, i.e., with 11.8% of instances being identified. In these cases, the prevalent reason is when reviewers cheer up authors that admit to a mistake, i.e., acknowledge a mistake. Apart from reducing commenting noise, our work suggests that emoji reactions play a positive role in facilitating collaborative communication during the review process.

DOI： 10.1007/s10664-023-10336-5

Web of Science

Scopus

出版者・発行元：Proceedings - 2023 IEEE/ACM 20th International Conference on Mining Software Repositories, MSR 2023  

Images are increasingly being shared by software developers in diverse channels including question-and-answer forums like Stack Overflow. Although prior work has pointed out that these images are meaningful and provide complementary information compared to their associated text, how images are used to support questions is empirically unknown. To address this knowledge gap, in this paper we specifically conduct an empirical study to investigate (I) the characteristics of images, (II) the extent to which images are used in different question types, and (III) the role of images on receiving answers. Our results first show that user interface is the most common image content and undesired output is the most frequent purpose for sharing images. Moreover, these images essentially facilitate the understanding of 68% of sampled questions. Second, we find that discrepancy questions are more relatively frequent compared to those without images, but there are no significant differences observed in description length in all types of questions. Third, the quantitative results statistically validate that questions with images are more likely to receive accepted answers, but do not speed up the time to receive answers. Our work demonstrates the crucial role that images play by approaching the topic from a new angle and lays the foundation for future opportunities to use images to assist in tasks like generating questions and identifying question-relatedness.

DOI： 10.1109/MSR59073.2023.00059

Web of Science

Scopus