一句话：提出 Falcon，用日志图表示与对比学习提升工业软件故障定位效果。
大规模软件与系统测试中，日志是故障定位的主要资源，但人工定位耗时昂贵。现有自动化方法多依赖细粒度特征，而工业日志往往缺乏这些信息，限制了适用性。本文提出 Falcon：将复杂语义日志组织为图表示，利用对比学习捕捉通过日志与失败日志的差异，识别关键故障相关特征；并设计基于传递分析的自适应图增强，降低故障无关日志信息对对比学习的干扰。实验与 34 种基于频谱和 4 种基于学习的方法比较，Falcon 均优于对比方法；在全球公司测试系统部署一个月，以文件级 Top-1 准确率成功定位 90 个故障中的 71 个。
Engineers utilize logs as a primary resource for fault localization in large-scale software and system testing, a process that is notoriously time-consuming, costly, and labor-intensive. Despite considerable progress in automated fault localization approaches, their applicability remains limited in such settings, due to the unavailability of fine-grained features in logs essential for most existing fault localization methods. In response, we introduce Falcon,a novel log-based fault localization framework. Falconorganizes complex semantic log information into graphical representations and employs contrastive learning to capture the differences between passed and failed logs, enabling the identification of crucial fault-related features. It also incorporates a specifically designed transitive analysis-based adaptive graph augmentation to minimize the influence of fault-unrelated log information on contrastive learning. Through extensive evaluations against 34 spectrum-based and 4 learning-based fault localization methods, Falcondemonstrates superior performance by out-performing all the methods in comparison. In addition, Falcondemonstrated its practical value by successfully identifying 71 out of 90 faults with a file-level Top-1 accuracy rate during a one-month deployment within a global company's testing system.
DOI 原文 ·
@inproceedings{paperbot8702,
title = {Enhancing Fault Localization in Industrial Software Systems via Contrastive Learning},
author = {Chun Li and Hui Li and Zhong Li and Minxue Pan and Xuandong Li},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00009}
}