arXiv subjects

Takahiro Monno

Publications and source records attributed to Takahiro Monno.

3 recordsLinked to original sources

arXiv2026

How Do Developers Use Migration Guides? A Case Study of Log4j

Migration guides are a form of software documentation that helps developers address breaking changes introduced in library version updates. Prior studies have examined documents such as release notes, API reference manuals, and patch notes. However, research that focuses specifically on migration guides remains limited. Improving the usability and coverage of migration guides is essential for helping developers resolve breaking changes efficiently. Yet, we still lack a clear understanding of how migration guides are currently provided and how developers use them in practice. To fill this gap, we first investigate whether libraries known to introduce incompatibilities provide migration guides. We then conduct a detailed case study on Log4j, a library that has experienced large-scale breaking updates in the past. We empirically analyze how developers refer to and use the official migration guide in real-world projects. We find that pull request authors most frequently reference the migration guide in the pull request description, and that most references (82.81%) link to the entire guide rather than specific sections. We also find that developers use migration guides not only during major version updates but also during subsequent maintenance tasks, suggesting that the guides serve as a resource throughout the entire migration process.

cs.SE↗

arXiv2026

How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses

The rapid adoption of large language models has led to the emergence of AI coding agents that autonomously create pull requests on GitHub. However, how these agents differ in their pull request description characteristics, and how human reviewers respond to them, remains underexplored. In this study, we conduct an empirical analysis of pull requests created by five AI coding agents using the AIDev dataset. We analyze agent differences in pull request description characteristics, including structural features, and examine human reviewer response in terms of review activity, response timing, sentiment, and merge outcomes. We find that AI coding agents exhibit distinct PR description styles, which are associated with differences in reviewer engagement, response time, and merge outcomes. We observe notable variation across agents in both reviewer interaction metrics and merge rates. These findings highlight the role of pull request presentation and reviewer interaction dynamics in human-AI collaborative software development.

cs.AI↗

arXiv2026

Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests

The automatic generation of pull requests (PRs) using AI agents has become increasingly common. Although AI-generated PRs are fast and easy to create, their merge rates have been reported to be lower than those created by humans. In this study, we conduct a large-scale empirical analysis of 40,214 PRs collected from the AIDev dataset. We extract 64 features across six families and fit statistical regression models to compare PR merge outcomes for human and agentic PRs, as well as across three AI agents. Our results show that submitter attributes dominate merge outcomes for both groups, while review-related features exhibit contrasting effects between human and agentic PRs. The findings of this study provide insights into improving PR quality through human-AI collaboration.

cs.SE↗