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Abstract:README files are critical for understanding and onboarding contributors to open-source software, yet they frequently become outdated. We formulate surgical documentation update recommendation as a task and present a Large Language Model-driven framework for use in a human-in-the-loop workflow. Given a pull request, the framework determines whether a README update is needed, identifies where changes should be made, and explains the triggering events. We evaluate the framework on 25,511 pull requests from 714 popular repositories. Its best configuration recovers half of the pull requests historically accompanied by README updates and achieves 28% user-facing accuracy under the observed prevalence of such updates. A qualitative failure analysis further identifies opportunities for improvement. We also conduct a retrospective study of 20 sampled repositories and a case study with a developer from a large open-source project. Manual annotation shows that 21.5% of temporally matched recommendations identify updates overlooked by developers, or 6.1% under the most conservative interpretation. These results suggest that the reported user-facing accuracy is a lower bound on likely deployment performance. Finally, we discuss implications for integrating documentation update tools into open-source development workflows.

Submission history

From: Haoyu Gao [view email]
[v1] Sat, 28 Feb 2026 06:04:45 UTC (151 KB)
[v2] Fri, 21 Aug 2026 03:28:27 UTC (322 KB)