arXiv：cs.AI· Celal Ziftci, Spencer Greene, Ray Liu, Livio Dalloro, Lorenzo Dini·· 7 小时前AI 评分64
Google 发布 FlowAgent 论文：低延迟智能体自动修复 CI 测试失败
Catching Developers in the Flow: Low-Latency Agentic Program Repair at Google Scale
AI 导读
Google 团队在 arXiv 论文（arXiv:2610.07289，被 ASE 2026 接收）中介绍 FlowAgent，一个部署在 Google 内部持续集成 pre-submit 流程中的 AI 智能体，用于自动修复测试失败，集成于内部工具 Critique 和 Cider。
正文
Abstract: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.
| Comments: | Accepted at the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026) |
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07289 [cs.SE] |
| (or arXiv:2610.07289v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07289 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Celal Ziftci [view email]
[v1]
Mon, 5 Oct 2026 19:23:06 UTC (1,551 KB)
来源：arXiv：cs.AI · arxiv.org