Agentic coding -- software development workflows in which autonomous coding agents plan, implement, and submit code changes with minimal human involvement -- is rapidly gaining traction. Prior work has shown that Pull Requests (PRs) produced using coding agents (Agentic-PRs) are accepted less often than PRs that are not labeled as agentic (Human-PRs). The rejection reasons for a single agent (Claude Code) have been explored, but a comparison of how rejection reasons differ between Agentic-PRs generated by different agents has not yet been performed. This comparison is important since different coding agents are often used for different purposes, which can lead to agent-specific failure patterns. In this paper, we inspect 654 rejected PRs from the AIDev dataset covering five coding agents, as well as a human baseline. Our results show that seven rejection modes occur only in Agentic-PRs, including distrust of AI-generated code. We also observe agent-specific patterns (e.g., automated withdrawal of inactive PRs by Devin), reflecting differences in how agents are configured and used in practice. Notably, a large proportion of rejected PRs (67.9%) lack explicit reviewer feedback, making their rejection reasons difficult to determine. To mitigate this issue, we propose a set of heuristics that reduce the proportion of such cases, offering a practical preprocessing step for future studies of PR rejection in agentic coding.

翻译：智能编码——即自主编码代理以最小人工参与规划、实现并提交代码变更的软件开发工作流——正迅速获得关注。先前研究表明，使用编码代理生成的拉取请求（智能PR）的接受率低于未标记为智能生成的人类PR。虽然已有研究探讨了单一代理（Claude Code）的拒绝原因，但尚未对不同代理生成的智能PR之间的拒绝原因差异进行比较。这种比较至关重要，因为不同的编码代理通常用于不同目的，可能导致特定于代理的失败模式。本文基于AIDev数据集，分析了涵盖五种编码代理及人类对照组的654个被拒PR。研究结果显示，有七种拒绝模式仅出现在智能PR中，包括对AI生成代码的不信任。我们还观察到代理特定的模式（例如Devin自动撤回非活跃PR），这反映了代理在实际配置和使用方式上的差异。值得注意的是，大部分被拒PR（67.9%）缺乏明确的审阅者反馈，导致其拒绝原因难以确定。为缓解此问题，我们提出了一套启发式规则，有效降低了此类案例的比例，为未来智能编码中PR拒绝研究提供了实用的预处理方法。