On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source

• URL: http://arxiv.org/abs/2601.13754v1
• Date: Tue, 20 Jan 2026 09:09:53 GMT
• Title: On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source
• Authors: Haoyu Gao, Peerachai Banyongrakkul, Hao Guan, Mansooreh Zahedi, Christoph Treude,
• Abstract summary: We investigated project-level guidelines and developers' interactions with AI-assisted pull requests (PRs)<n>We found that over 67.5% of AI-co-authored PRs originate from contributors without prior code ownership.<n>In contrast to human-created PRs where non-owner developers receive the most feedback, AI-co-authored PRs from non-owners receive the least.
• Score: 11.412808537439973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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