LLM-SrcLog: Towards Proactive and Unified Log Template Extraction via Large Language Models

• URL: http://arxiv.org/abs/2512.04474v1
• Date: Thu, 04 Dec 2025 05:30:15 GMT
• Title: LLM-SrcLog: Towards Proactive and Unified Log Template Extraction via Large Language Models
• Authors: Jiaqi Sun, Wei Li, Heng Zhang, Chutong Ding, Shiyou Qian, Jian Cao, Guangtao Xue,
• Abstract summary: LLM-SrcLog is a proactive and unified framework for log template parsing.<n>It extracts templates directly from source code prior to deployment.<n>It supplements them with data-driven parsing for logs without available code.
• Score: 19.933913707655467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Log parsing transforms raw logs into structured templates containing constants and variables. It underpins anomaly detection, failure diagnosis, and other AIOps tasks. Current parsers are mostly reactive and log-centric. They only infer templates from logs, mostly overlooking the source code. This restricts their capacity to grasp dynamic log structures or adjust to evolving systems. Moreover, per-log LLM inference is too costly for practical deployment. In this paper, we propose LLM-SrcLog, a proactive and unified framework for log template parsing. It extracts templates directly from source code prior to deployment and supplements them with data-driven parsing for logs without available code. LLM-SrcLog integrates a cross-function static code analyzer to reconstruct meaningful logging contexts, an LLM-based white-box template extractor with post-processing to distinguish constants from variables, and a black-box template extractor that incorporates data-driven clustering for remaining unmatched logs. Experiments on two public benchmarks (Hadoop and Zookeeper) and a large-scale industrial system (Sunfire-Compute) show that, compared to two LLM-based baselines, LLM-SrcLog improves average F1-score by 2-17% and 8-35%. Meanwhile, its online parsing latency is comparable to data-driven methods and about 1,000 times faster than per-log LLM parsing. LLM-SrcLog achieves a near-ideal balance between speed and accuracy. Finally, we further validate the effectiveness of LLM-SrcLog through practical case studies in a real-world production environment.

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