Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2026

Abstract

Large Language Models (LLMs) increasingly automate software engineering tasks. While recent studies highlight the accelerated adoption of “AI as a teammate” in Open Source Software (OSS), developer interaction patterns remain under-explored. In this work, we investigated project-level guidelines and developers’ interactions with AI-assisted pull requests (PRs) by expanding the AIDev dataset to include finer-grained contributor code ownership and a comparative baseline of human-created PRs. We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership. Despite this, the majority of repositories lack guidelines for AI-coding agent usage. Notably, we observed a distinct interaction pattern: AI-co-authored PRs are merged significantly faster with minimal feedback. In contrast to human-created PRs where non-owner developers receive the most feedback, AI-co-authored PRs from non-owners receive the least, with approximately 80% merged without any explicit review. Finally, we discuss implications for developers and researchers.

Keywords

AI-Human Collaboration, Code Review, Documentation

Discipline

Artificial Intelligence and Robotics | Software Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

MSR '26: Proceedings of the 23rd International Conference on Mining Software Repositories, Rio de Janeiro, Brazil, April 13-14

First Page

777

Last Page

781

ISBN

9798400724749

Identifier

10.1145/3793302.3793573

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3793302.3793573

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