LLMを活用したREADMEメンテナンス：更新の必要性を探る

The README file serves as a critical source of information for gaining an overview and helping developers onboard to an Open Source Software (OSS) project. Yet, documentation issues persist; in particular, ``outdated'' documentation is perceived by developers as one of the most frequent and severe challenges with gaining project understanding. While previous studies have aimed to mitigate this problem, they typically either rely on highly-engineered solutions focused on specific code components or employ generative methods that are ineffective for incremental maintenance. In this study, we propose a lightweight Large Language Model (LLM)-driven approach to facilitate precise, localised README file updates within a human-in-the-loop workflow. Specifically, given a pull request (PR), our pipeline determines whether an update is necessary; if so, it identifies the precise locations where updates should be applied and provides a justification based on the triggering events. Our evaluation on 27,772 PRs across 714 popular repositories demonstrates high precision and utility. Furthermore, we performed a qualitative failure case analysis to provide deeper insights and directions for improvement. We also conducted a retrospective study on 20 sampled repositories, complemented by a case study with a developer of a large OSS project. These evaluations demonstrate that the tool effectively identifies overlooked PRs requiring README updates, thereby helping to mitigate the risk of outdated documentation. Finally, we provide concrete implications for practitioners and researchers, highlighting the need to further explore effective interaction patterns to incorporate documentation update tools into the OSS development workflow.