Three papers co-authored by Lecturer Kazumasa Shimari were presented at MSR 2026 (April 13–14) and IDE 2026 (April 18) in Rio de Janeiro, Brazil.
How AI Coding Agents Communicate: A Study of Pull Request Characteristics and Human Review Responses
Authors: Kan Watanabe, Rikuto Tsuchida, Takahiro Monno, Bin Huang, Kazuma Yamasaki, Youmei Fan, Kazumasa Shimari, Kenichi Matsumoto
Publication / venue: Proceedings of the 23rd International Conference on Mining Software Repositories, pp. 878–882, 2026.
Abstract
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.
Source: Abstract
Presented on April 13, 2026. Official MSR program. The abstract is reproduced in full from the authors’ preprint, whose title also includes the word “Description”.
Who Writes the Docs in SE 3.0? Agent vs. Human Documentation Pull Requests
Authors: Kazuma Yamasaki, Joseph Ayobami Joshua, Tasha Settewong, Mahmoud Alfadel, Kazumasa Shimari, Kenichi Matsumoto
Publication / venue: Proceedings of the 23rd International Conference on Mining Software Repositories, pp. 974–978, 2026.
Abstract
As software engineering moves toward SE3.0, AI agents are increasingly used to carry out development tasks and contribute changes to software projects. It is therefore important to understand the extent of these contributions and how human developers review and intervene, since these factors shape the risks of delegating work to AI agents. While recent studies have examined how AI agents support software development tasks (e.g., code generation, issue resolution, and PR automation), their role in documentation tasks remains underexplored-even though documentation is widely consumed and shapes how developers understand and use software. Using the AIDev, we analyze 1,997 documentation-related pull requests (PRs) authored by AI agents and human developers, where documentation PRs are those that create or modify project documentation artifacts. We find that AI agents submit substantially more documentation-related PRs than humans in the studied repositories. We further observe that agent-authored documentation edits are typically integrated with little follow-up modification from humans, raising concerns about review practices and the reliability of agent-generated documentation. Overall, while AI agents already contribute substantially to documentation workflows, our results suggest concerns for emerging challenges for documentation quality assurance and human-AI collaboration in SE3.0.
Source: Abstract
Presented on April 13, 2026. Official MSR program.
Forecasting Developer Environments with GenAI: A Research Perspective
Authors: Raula Gaikovina Kula, Christoph Treude, Xing Hu, Sebastian Baltes, Earl T. Barr, Kelly Blincoe, Fabio Calefato, Junjie Chen, Marc Cheong, Youmei Fan, Daniel M. German, Marco Gerosa, Jin L.C. Guo, Shinpei Hayashi, Robert Hirschfeld, Reid Holmes, Yintong Huo, Takashi Kobayashi, Michele Lanza, Zhongxin Liu, Olivier Nourry, Nicole Novielli, Denys Poshyvanyk, Shinobu Saito, Kazumasa Shimari, Igor Steinmacher, Mairieli Wessel, Markus Wagner, Annie Vella, Laurie Williams, Xin Xia
Publication / venue: Proceedings of the 3rd ACM/IEEE International Workshop on Integrated Development Environments, pp. 1–5, 2026.
Abstract
Generative Artificial Intelligence (GenAI) models are achieving remarkable performance in various tasks, including code generation, testing, code review, and program repair. The ability to increase the level of abstraction away from writing code has the potential to change the Human-AI interaction within the integrated development environment (IDE). To explore the impact of GenAI on IDEs, 33 experts from the Software Engineering, Artificial Intelligence, and Human-Computer Interaction domains gathered to discuss challenges and opportunities at Shonan Meeting 222, a four-day intensive research meeting. Four themes emerged as areas of interest for researchers and practitioners.
Source: Abstract
Presented on April 18, 2026. Co-author’s publication record, Paper DOI.