The increasing adoption of AI coding agents has increased the number of agent-generated pull requests (PRs) merged with little or no human intervention. Although such PRs promise productivity gains, their post-merge code quality remains underexplored, as prior work has largely relied on benchmarks and controlled tasks rather than large-scale post-merge analyses. To address this gap, we analyze 1,210 merged agent-generated bug-fix PRs from Python repositories in the AIDev dataset. Using SonarQube, we perform a differential analysis between base and merged commits to identify code quality issues newly introduced by PR changes. We examine issue frequency, density, severity, and rule-level prevalence across five agents. Our results show that apparent differences in raw issue counts across agents largely disappear after normalizing by code churn, indicating that higher issue counts are primarily driven by larger PRs. Across all agents, code smells dominate, particularly at critical and major severities, while bugs are less frequent but often severe. Overall, our findings show that merge success does not reliably reflect post-merge code quality, highlighting the need for systematic quality checks for agent-generated bug-fix PRs.

翻译：随着AI编码智能体的日益普及，由智能体生成且未经或仅经少量人工干预即被合并的拉取请求（PR）数量不断增长。尽管此类PR有望提升开发效率，但其合并后的代码质量尚未得到充分探究，因为先前研究主要依赖基准测试和受控任务，而非大规模合并后分析。为填补这一空白，我们分析了来自AIDev数据集中Python代码库的1,210个已合并的智能体生成错误修复PR。利用SonarQube工具，我们对基准提交与合并提交进行差异分析，以识别由PR变更新引入的代码质量问题。我们考察了五种智能体在问题频率、密度、严重程度及规则层级普遍性上的表现。研究结果表明，在根据代码变更量进行标准化处理后，不同智能体在原始问题数量上的显著差异基本消失，这说明较高的问题数量主要由较大规模的PR所导致。在所有智能体中，代码异味占主导地位，尤其在关键和主要严重级别上，而程序错误虽出现频率较低但通常更为严重。总体而言，我们的发现表明合并成功并不能可靠反映合并后的代码质量，这凸显了对智能体生成错误修复PR进行系统性质量检查的必要性。