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arXiv · 2607.21079

Maintenance Signals in AI-Assisted GitHub Repositories: Evidence from GenAI Adopters

Rikuto Tsuchida·Youmei Fan·Kazumasa Shimari·Raula Gaikovina Kula·Gema Rodríguez-Pérez·Kenichi Matsumoto
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Abstract

Generative artificial intelligence (GenAI) can reduce code-generation effort, but it may shift work to documentation, validation, debugging, and maintenance. We study observable maintenance-cost signals among GenAI adopters on GitHub by analyzing 622 users who publicly signal adoption, 179 repositories with visible AI-assistance configuration files, 179 matched traditional repositories, and 248 issues created in AI-assisted repositories. AI-assisted repositories span diverse project types and contain longer README files with more headers and code blocks, while traditional repositories contain more external URLs. Issues concerning GenAI technology often involve external dependencies, such as API rate limits and reliance on GenAI provider APIs. These findings suggest that AI assistance shifts maintenance costs toward verifying generated content, managing external AI dependencies, and validating AI-specific behavior.

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Rikuto Tsuchida, Youmei Fan, Kazumasa Shimari, Raula Gaikovina Kula, Gema Rodríguez-Pérez, Kenichi Matsumoto. 2026-07-23. Maintenance Signals in AI-Assisted GitHub Repositories: Evidence from GenAI Adopters. https://arxiv.org/abs/2607.21079

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