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

2026-07-23

AI Summary

This study investigates whether generative artificial intelligence (GenAI) shifts the burden of software development from coding to documentation, verification, and maintenance tasks. Drawing on a dataset of 622 GitHub users, 179 GenAI-assisted repositories matched with traditional counterparts, and 248 related issues, the authors employ quantitative content analysis, metadata mining, and textual analysis to empirically examine the impact of GenAI adoption on open-source project maintenance activities. The findings reveal that GenAI-assisted repositories feature longer and more structurally complex README files but fewer external links. Moreover, associated issues are significantly concentrated on API limitations and external dependency management, indicating a shift in maintenance focus toward validating generated content and governing dependencies—thereby uncovering new dimensions of maintenance introduced by AI integration.

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.

Problem

• maintenance cost
• generative AI
• software maintenance
• AI-assisted development
• GitHub repositories

Innovation

• Generative AI
• software maintenance
• GitHub repositories
• AI-assisted development
• maintenance cost