发表机构
Microsoft Research; University of Illinois Urbana-Champaign; Inception（微软研究院; 伊利诺伊大学厄巴纳-香槟分校; Inception）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对云事件手动创建故障排除指南的问题，提出FixItFlow系统，利用大语言模型从历史事件数据生成指南，能提取诊断模式、合成结构化指南并严格验证，经评估可提升事件响应，减轻团队文档负担。
AI 中文摘要
云服务频繁出现需要快速诊断和解决的事件。故障排除指南有助于工程师一致地做出响应，但手动创建指南劳动强度大，导致覆盖不完整和文档过时。我们提出了FixItFlow，这是一个使用大语言模型从历史事件数据生成故障排除指南的自动化系统。该系统从工程师操作中提取诊断模式，合成带有经过验证命令的结构化指南，并进行严格验证以防止虚假内容。在对26名工程师的评估中，生成的指南在清晰度方面获得了61.5%的正面评价，并且对于有相关指南的事件，缓解时间减少了2.3倍。这些结果表明，自动指南生成可以改善事件响应，同时减轻工程团队的文档负担。
英文摘要
Cloud services experience frequent incidents that require rapid diagnosis and resolution. Troubleshooting guides help engineers respond consistently, but creating them manually is labor-intensive, resulting in incomplete coverage and outdated documentation. We present FixItFlow, an automated system that generates troubleshooting guides from historical incident data using large language models. The system extracts diagnostic patterns from engineer actions, synthesizes structured guides with verified commands, and enforces strict validation to prevent fabricated content. In our evaluation with 26 engineers, generated guides achieved 61.5\% positive ratings for clarity and demonstrated a 2.3x reduction in mitigation time for incidents with associated guides. These results indicate that automated guide generation can improve incident response while reducing documentation burden on engineering teams.
