一句话：提出配置日志思想，用配置感知静态污点分析与LLM生成诊断日志，显著提升系统配置问题的可诊断性。
现代可配置系统（如分布式系统与Web 3.0去中心化系统）配置空间复杂，误配置频发。现有可诊断性支持多在故障后分析行为，未关注软件是否记录足够失败信息。本文提出配置日志思想并实现工具ConfLogger，将配置感知静态污点分析与基于LLM的日志生成结合：追踪配置相关数据流以识别配置敏感代码段，再分析配置代码上下文生成诊断日志。在八个系统上评估，增强日志使基于日志的误配置诊断工具在30个静默误配置场景中错误定位准确率达100%，80%可直接通过显式配置信息解决；日志点覆盖率达74%，优于基线LLM日志器12%与30%；变量日志精度、召回率、F1分别提升8.6%、79.3%、26.2%。22案例用户研究显示诊断时间加速1.25倍，准确率提升251.4%。
Modern configurable systems, including distributed systems and recently popular decentralized systems in Web 3.0, offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent software bugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clues sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies configuration-sensitive code segments by tracing configuration-related data flow in the whole project, and 2) generates diagnostic logging statements by analyzing configuration code contexts. Evaluation results on eight popular software systems demonstrate the effectiveness of ConfLogger to enhance configuration diagnosability. Specifically, ConfLogger-enhanced logs successfully aid a log-based misconfiguration diagnosis tool to achieve 100% accuracy on error localization in 30 silent misconfiguration scenarios, with 80% directly resolvable through explicit configuration information exposed. In addition, ConfLogger achieves 74% coverage of existing logging points, outperforming baseline LLM-based loggers by 12% and 30%. It also gains 8.6% higher in precision, 79.3% higher in recall, and 26.2% higher in F1 compared to the state-of-the-art baseline in terms of variable logging while also augmenting diagnostic value. A controlled user study on 22 cases further validated its utility, speeding up diagnostic time by 1.25× and improving troubleshooting accuracy by 251.4%.
@inproceedings{abs-2508-20977,
title = {ConfLogger: Enhance Systems' Configuration Diagnosability through Configuration Logging},
author = {Shiwen Shan and Yintong Huo and Yuxin Su and Zhining Wang and Dan Li and Zibin Zheng},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3764570}
}