发表机构
Google（谷歌）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FlowAgent是谷歌部署的AI智能体，通过ReAct循环和弃权过滤器在低延迟下修复提交前测试失败，人工评估准确率67.18%，已对295,508个变更提出建议，其中28,554个被应用。
AI 中文摘要
手动修复程序故障对于软件开发者而言既耗时又干扰工作，尤其是在提交前阶段，持续集成系统中出现测试失败时。尽管通过大型语言模型，自动程序修复已取得显著进展，但现有最先进技术主要聚焦于提交后工作流，以离线方式运行，缺乏在开发者切换上下文之前实时协助其工作流所需的低延迟要求。在本文中，我们介绍FlowAgent，一个部署于谷歌的AI智能体，用于在持续集成系统中的提交前外层循环工作流中自动修复测试失败。FlowAgent集成于谷歌内部开发者工具Critique和Cider中，采用ReAct风格的生成-验证循环，以及严格的执行前和执行后弃权（不执行）过滤器，以确保在严格延迟约束下提供高质量建议。基于我们的案例研究，FlowAgent非常有效。首先，对195个真实世界测试失败进行的人工评估显示，其建议正确修复的准确率为67.18%。在其谷歌全范围部署后，FlowAgent对295,508个变更提出了修复建议，其中开发者预览了65,069个，应用了28,554个。来自访谈的开发者反馈表明，该智能体在建议正确修复方面很有用，自主修复智能体融入工业软件工程工作流受到良好接受，同时仍存在有趣的挑战和机遇。
英文摘要
Manual repair of program failures is time-consuming and disruptive for software developers, particularly during the pre-submit phase where test failures occur within continuous integration systems. While Automated Program Repair has seen significant advancement through Large Language Models, existing state-of-the-art techniques primarily focus on post-submit workflows, operating offline without the low-latency requirements necessary to assist developers in real-time within their flow before they switch context. In this paper, we introduce FlowAgent, an AI agent deployed at Google to automatically repair test failures in the pre-submit outer-loop workflow inside continuous integration systems. Integrated into Google's internal developer tools, Critique and Cider,FlowAgent utilizes a ReAct-style generate-and-validate loop, as well as rigorous pre-execution and post-execution abstention filters to ensure high-quality suggestions under strict latency constraints. Based on our case studies, FlowAgent is highly effective. First, a manual evaluation conducted on 195 real-world test failures demonstrated 67.18% accuracy in suggesting correct fixes. Following its Google-wide deployment, FlowAgent suggested fixes on 295,508changes, of which developers previewed 65,069 and applied 28,554. Developer feedback from interviews indicate that the agent is useful in suggesting correct fixes, integration of autonomous repair agents into industrial software engineering workflows is received well, while interesting challenges and opportunities still remain.
CommentsAccepted at the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026)
