整合崩溃报告挖掘与大语言模型（LLMs）用于缺陷定位与修复：一份工业报告

中文摘要 AI 辅助

分析大规模工业软件系统中的崩溃报告缺陷需要大量维护工作，尤其是在生产环境中，开发人员必须处理大量崩溃报告和源代码构件以定位并修复其根本原因。尽管近期研究表明大语言模型（LLMs）可辅助维护任务，但关于其在工业场景中支持开发人员分析崩溃报告缺陷及修复与多组崩溃报告相关缺陷的有效性，目前尚不清楚。为填补该空白，本研究探究将崩溃报告挖掘技术——具体为堆栈跟踪聚类、可疑文件与方法排名——与LLMs整合，是否可支持生产环境中的崩溃定位与修复。我们在四种提示配置下对五种LLMs开展回顾性评估，随后选择最优模型，在从两个大型Java企业系统收集的38个崩溃缺陷上运行该模型。我们进一步分析LLM生成响应的结构特征与解释模式，并通过人工验证评估定位与修复有效性。结果显示，最优配置在全数据集上可定位多达71%的崩溃缺陷，正确修复52%的崩溃缺陷。这些发现提供实证证据，表明将崩溃报告挖掘与基于LLM的修复相结合，可有效支持工业维护工作流程中的调试活动。

英文摘要

Analyzing crash-report bugs in large-scale industrial software systems requires substantial maintenance effort, particularly in production environments where developers must handle large volumes of crash reports and source code artifacts to localize and fix their root causes. While recent studies have shown that Large Language Models (LLMs) can assist with maintenance tasks, little is known about their effectiveness in supporting developers in analyzing crash-report bugs and repairing bugs associated with groups of crash reports in industrial settings. To address this gap, we investigate whether integrating crash report mining techniques---specifically stack trace clustering and suspicious file and method ranking---with LLMs can support crash localization and repair in production environments. We conduct a retrospective evaluation of five LLMs under four prompt configurations. After that, we chose the best model to run on 38 crash bugs collected from two large Java enterprise systems. We further analyze the structural characteristics and explanatory patterns of LLM-generated responses and assess localization and repair effectiveness through manual validation. Our results show that the best-performing configuration localizes up to 71% and correctly repairs 52% of crash bugs on the full dataset. These findings provide empirical evidence that combining crash report mining with LLM-based repair can effectively support debugging activities in industrial maintenance workflows.

发表机构

• Federal University of Rio Grande do Norte（北里奥格兰德联邦大学）
• Singapore Management University（新加坡管理大学）
• Federal University of Pernambuco（伯南布哥联邦大学）

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