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[2602.17084] How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses
Summary
This study analyzes how AI coding agents create pull request descriptions and how human reviewers respond, revealing distinct styles and their impact on engagement and merge outcomes.
Why It Matters
As AI coding agents become more prevalent in software development, understanding their communication styles and the effects on human reviewer interactions is crucial. This research provides insights into optimizing collaboration between AI and human developers, potentially improving software quality and development efficiency.
Key Takeaways
- AI coding agents exhibit unique styles in pull request descriptions.
- Different styles affect reviewer engagement and response times.
- Variations in merge rates highlight the importance of presentation in AI-human collaboration.
Computer Science > Artificial Intelligence arXiv:2602.17084 (cs) [Submitted on 19 Feb 2026] Title:How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses Authors:Kan Watanabe, Rikuto Tsuchida, Takahiro Monno, Bin Huang, Kazuma Yamasaki, Youmei Fan, Kazumasa Shimari, Kenichi Matsumoto View a PDF of the paper titled How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses, by Kan Watanabe and 6 other authors View PDF HTML (experimental) 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 re...