让AI可见而非消失：AI政策如何重塑GitHub上的开发者体验
Making AI Visible, Not Vanished: How AI Policies Reshape Developer Experience on GitHub
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中文总结 AI 辅助
本研究通过分析29624个GitHub代码库，提出TRACE框架，发现AI治理主要规范而非禁止AI辅助开发，强调透明度与责任性的政策效果更优。
中文摘要 AI 辅助
生成式AI正迅速重塑开源软件（OSS）的开发工作，促使各项目引入规范AI辅助贡献的政策。然而，人们对这些政策的差异及其是否影响开发者体验知之甚少。我们开展了首个针对OSS中AI治理政策的大规模实证研究，分析了29624个GitHub代码库，识别出385个采用AI政策的项目，并推导了TRACE框架，该框架涵盖五大治理维度：透明度（Transparency）、责任性（Responsibility）、归属（Attribution）、约束（Constraints）与执行（Enforcement）。我们进一步将政策分为五大治理类别，并采用倾向得分匹配与双重差分法纵向分析其效果。结果显示，AI治理主要是规范而非禁止AI辅助开发；政策采用带来了维护者参与度提升、AI披露增加、评审互动更丰富及代码质量改善，同时AI辅助贡献持续增长。治理设计至关重要：强调透明度与责任性的政策，比单纯的限制性政策产生了更强的社区与质量成果。我们的研究揭示了不同AI治理策略如何塑造开发者体验，并为OSS社区设计有效AI政策提供了证据支持。
英文摘要
Generative AI is rapidly reshaping Open Source Software (OSS) software development,prompting projects to introduce policies governing AI-assisted contributions. However, little is known about how these policies differ or whether they influence developer experience. We present the first large-scale empirical study of AI governance policies in OSS. Analyzing 29,624 GitHub repositories, we identify 385 projects that adopted AI policies and derive TRACE, a framework capturing five governance dimensions: Transparency, Responsibility, Attribution, Constraints, and Enforcement. We further classify policies into five governance families and estimate their effects using propensity-score matching and longitudinal difference-in-differences analysis. Our results show that AI governance primarily regulates rather than prohibits AI-assisted development. Policy adoption brings maintainer engagement, increased AI disclosure, richer review interactions, and improved code quality while AI-assisted contributions continue to grow. Governance design matters: policies emphasizing transparency and responsibility produced stronger community and quality outcomes than restrictive approaches alone. Our findings show how different AI governance strategies shape developer experience and provide evidence to help OSS communities design effective AI policies.
