Does My README File Need To Be Updated? Exploring LLM-Based README Maintenance

2026-02-28

AI Summary

This work addresses the prevalent issue of outdated README files in open-source projects, which often lag behind code changes and hinder developer comprehension and adoption. To tackle this, the authors propose a lightweight, human-in-the-loop approach that leverages large language models (LLMs) during pull request merges to automatically determine whether a README update is necessary, precisely identify the relevant sections requiring modification, and provide justifications for those changes. This method represents the first application of LLMs to incremental, fine-grained README maintenance, effectively balancing automation efficiency with developer control. Evaluation across 714 repositories and 27,772 pull requests demonstrates high precision, and case studies further confirm its capability to uncover otherwise overlooked README update needs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Summarization
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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 Yet, documentation issues persist; in particular, ``outdated'' documentation is perceived by developers as one of the most frequent and severe challenges with gaining project understanding.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.

Problem

• README
• outdated documentation
• Open Source Software
• documentation maintenance
• pull request

Innovation

• LLM-based documentation
• README maintenance
• pull request analysis
• human-in-the-loop
• software documentation update