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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.18205 |
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| _version_ | 1866910893717061632 |
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| author | Zhang, Wei Guan, Xiangyuan Yunhong, Lu Zhang, Jie Song, Shuangyong Cheng, Xianfu Wu, Zhenhe Li, Zhoujun |
| author_facet | Zhang, Wei Guan, Xiangyuan Yunhong, Lu Zhang, Jie Song, Shuangyong Cheng, Xianfu Wu, Zhenhe Li, Zhoujun |
| contents | Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to identify the correct templates due to reliance on human-made rules. Besides, these methods focus on statistical features while ignoring semantic information in log messages. To address these challenges, we introduce a cutting-edge \textbf{L}og parsing framework with \textbf{E}ntropy sampling and chain-of-thought \textbf{M}erging (\model{}). Specifically, to discard the tedious manual rules, we propose a novel sampling method inspired by information entropy, which efficiently clusters typical logs. Furthermore, to enhance the merging of log templates, we design a chain-of-thought method for large language models (LLMs). LLMs exhibit exceptional semantic comprehension and deftly distinguish between parameters and invariant tokens. We have conducted experiments on large-scale public datasets. Extensive evaluation demonstrates that \model{} achieves state-of-the-art performance and impressive efficiency. The Code is available at https://github.com/zwpride/lemur. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging Zhang, Wei Guan, Xiangyuan Yunhong, Lu Zhang, Jie Song, Shuangyong Cheng, Xianfu Wu, Zhenhe Li, Zhoujun Software Engineering Artificial Intelligence Logs produced by extensive software systems are integral to monitoring system behaviors. Advanced log analysis facilitates the detection, alerting, and diagnosis of system faults. Log parsing, which entails transforming raw log messages into structured templates, constitutes a critical phase in the automation of log analytics. Existing log parsers fail to identify the correct templates due to reliance on human-made rules. Besides, these methods focus on statistical features while ignoring semantic information in log messages. To address these challenges, we introduce a cutting-edge \textbf{L}og parsing framework with \textbf{E}ntropy sampling and chain-of-thought \textbf{M}erging (\model{}). Specifically, to discard the tedious manual rules, we propose a novel sampling method inspired by information entropy, which efficiently clusters typical logs. Furthermore, to enhance the merging of log templates, we design a chain-of-thought method for large language models (LLMs). LLMs exhibit exceptional semantic comprehension and deftly distinguish between parameters and invariant tokens. We have conducted experiments on large-scale public datasets. Extensive evaluation demonstrates that \model{} achieves state-of-the-art performance and impressive efficiency. The Code is available at https://github.com/zwpride/lemur. |
| title | Lemur: Log Parsing with Entropy Sampling and Chain-of-Thought Merging |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2402.18205 |