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Documentation Drift in AI-Generated Pull Requests

Will De Michele, Kla Tantithamthavorn · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27381 · cited by 0 works (OpenAlex) · open-access PDF

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Агенты по кодированию ИИ в настоящее время вносят свой вклад в программные хранилища в масштабе, но их методы документации остаются неохарактеризованными. Мы анализируем 29 669 запросов на погружение, генерируемых ИИ, из 2,585 хранилищ в наборе данных AIDev, измеряя документационный кообмен: сопровождаются ли модификации кодовых файлов обновлением документационного файла в тех же PR. Мы обнаруживаем, что 63,2% PR изменяют код без обновления какой-либо документации. Это резко варьируется в зависимости от типа задачи: 55,2% для функций и 77,5% для пересмотра. Недокументированные кодовые файлы сопровождаются исправлением того же файла с 8,6 до 11,7% чаще, чем

Machine translation (Meta M2M100); the English original follows.

Abstract

AI coding agents now contribute to software repositories at scale, yet their documentation practices remain uncharacterised. We analyse 29,669 AI-generated pull requests from 2,585 repositories in the AIDev dataset, measuring documentation co-change: whether code-file modifications are accompanied by a documentation-file update in the same PR. We find that 63.2% of AI PRs change code without updating any documentation. This varies sharply by task type: 55.2% for features and 77.5% for refactoring. Undocumented code files are followed by a fix to the same file 8.6 to 11.7% more often than documented files across 30 to 90-day windows. A raw PR-level comparison shows the opposite because documented PRs are 3.9 times larger and therefore attract more fixes. Controlling for size exposure reverses this result. Documented changes also merge faster despite their larger size (median: 40 seconds versus 184 seconds), although merging is largely automated in this dataset. Documentation drift also compounds: within 90 days, a file changed without documentation is later re-documented at roughly half the rate of a file changed with documentation (24% versus 48%). This suggests that documentation debt tends to accumulate rather than be revisited.

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Topics: Documentation, Merge (version control), Computer science, Internal documentation, Task (project management), Coding (social sciences)

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