The automatic generation of pull requests (PRs) using AI agents has become increasingly common. Although AI-generated PRs are fast and easy to create, their merge rates have been reported to be lower than those created by humans. In this study, we conduct a large-scale empirical analysis of 40,214 PRs collected from the AIDev dataset. We extract 64 features across six families and fit statistical regression models to compare PR merge outcomes for human and agentic PRs, as well as across three AI agents. Our results show that submitter attributes dominate merge outcomes for both groups, while review-related features exhibit contrasting effects between human and agentic PRs. The findings of this study provide insights into improving PR quality through human-AI collaboration.

翻译：利用AI智能代理自动生成拉取请求（PR）已日益普遍。尽管AI生成的PR创建速度快且易于生成，但其合并率据报道低于人类创建的PR。本研究对从AIDev数据集中收集的40,214个PR进行了大规模实证分析。我们提取了涵盖六个类别的64项特征，并拟合统计回归模型以比较人类PR与智能代理PR的合并结果，以及三种不同AI代理之间的差异。研究结果表明，提交者属性对两组PR的合并结果均起主导作用，而评审相关特征在人类PR与智能代理PR之间则呈现相反的影响效应。本研究的发现为通过人机协作提升PR质量提供了重要见解。