We present the first empirical study of AI-generated pull requests that are 'silent,' meaning no comments or discussions accompany them. This absence of any comments or discussions associated with such silent AI pull requests (SPRs) poses a unique challenge in understanding the rationale for their acceptance or rejection. Hence, we quantitatively study 4,762 SPRs of five AI agents made to popular Python repositories drawn from the AIDev public dataset. We examine SPRs impact on code complexity, other quality issues, and security vulnerabilities, especially to determine whether these insights can hint at the rationale for acceptance or rejection of SPRs.

翻译：本研究首次对“静默”AI生成拉取请求（即未伴随任何评论或讨论的请求）开展实证分析。此类静默AI拉取请求（SPR）因缺乏关联讨论，为理解其接受或拒绝的决策依据带来了独特挑战。为此，我们基于AIDev公共数据集，从热门Python代码库中选取五个AI代理生成的4,762个SPR进行定量研究。通过考察SPR对代码复杂度、其他质量问题及安全漏洞的影响，我们特别探究这些发现能否为SPR的接受或拒绝决策提供解释依据。