一句话：提出 FAIL，用 LLM 自动分析新闻中的软件故障，以替代人工并扩大回顾性故障分析规模。
软件故障分析可为工程实践、标准与法规提供依据，例如 Log4J 漏洞推动了对软件供应链安全的关注。但私有工程记录难以获取，因此回顾性故障分析常依赖新闻报道；此前该类分析多靠人工，导致分析规模有限，社区缺乏能覆盖广泛新闻来源与事件的自动化支持。本文提出 FAIL，利用大语言模型从新闻中自动分析软件故障，以期支撑更大规模的回顾性故障研究。摘要未报告具体实现细节或评估结果，主要贡献是提出这一 LLM 驱动的自动化分析方案。
Software failures inform engineering work, standards, regulations. For example, the Log4J vulnerability brought government and industry attention to evaluating and securing software supply chains. Retrospective failure analysis is thus a valuable line of software engineering research. Accessing private engineering records is difficult, so such analyses tend to use information reported by the news media. However, prior works in this direction have relied on manual analysis. That has limited the scale of their analyses. The community lacks automated support to enable such analyses to consider a wide range of news sources and incidents.