Unleashing the True Potential of Semantic-Based Log Parsing with Pre-Trained Language Models
Software-intensive systems often produce console logs for troubleshooting purposes. Log parsing, which aims at parsing a log message into a specific log template, typically serves as the first step toward automated log analytics. To better comprehend the semantic information of log messages, many se...
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| Published in: | Proceedings / International Conference on Software Engineering pp. 975 - 987 |
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| Main Authors: | , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
26.04.2025
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| ISSN: | 1558-1225 |
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| Abstract | Software-intensive systems often produce console logs for troubleshooting purposes. Log parsing, which aims at parsing a log message into a specific log template, typically serves as the first step toward automated log analytics. To better comprehend the semantic information of log messages, many semantic-based log parsers have been proposed. These log parsers fine-tune a small pre-trained language model (PLM) such as RoBERTa on a few labelled log samples. With the increasing popularity of large language models (LLMs), some recent studies also propose to leverage LLMs such as ChatGPT through in-context learning for automated log parsing and obtain better results than previous semantic-based log parsers with small PLMs. In this paper, we show that semantic-based log parsers with small PLMs can actually achieve better or comparable performance to state-of-the-art LLM-based log parsing models while being more efficient and cost-effective. We propose Unleash, a novel semantic-based log parsing approach, which incorporates three enhancement methods to boost the performance of PLMs for log parsing, including (1) an entropy-based ranking method to select the most informative log samples; (2) a contrastive learning method to enhance the fine-tuning process; and (3) an inference optimization method to improve the log parsing performance. We evaluate Unleash on a set of large-scale, public log datasets and the experimental results show that Unleash is effective and efficient compared to state-of-the-art log parsers. |
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| AbstractList | Software-intensive systems often produce console logs for troubleshooting purposes. Log parsing, which aims at parsing a log message into a specific log template, typically serves as the first step toward automated log analytics. To better comprehend the semantic information of log messages, many semantic-based log parsers have been proposed. These log parsers fine-tune a small pre-trained language model (PLM) such as RoBERTa on a few labelled log samples. With the increasing popularity of large language models (LLMs), some recent studies also propose to leverage LLMs such as ChatGPT through in-context learning for automated log parsing and obtain better results than previous semantic-based log parsers with small PLMs. In this paper, we show that semantic-based log parsers with small PLMs can actually achieve better or comparable performance to state-of-the-art LLM-based log parsing models while being more efficient and cost-effective. We propose Unleash, a novel semantic-based log parsing approach, which incorporates three enhancement methods to boost the performance of PLMs for log parsing, including (1) an entropy-based ranking method to select the most informative log samples; (2) a contrastive learning method to enhance the fine-tuning process; and (3) an inference optimization method to improve the log parsing performance. We evaluate Unleash on a set of large-scale, public log datasets and the experimental results show that Unleash is effective and efficient compared to state-of-the-art log parsers. |
| Author | Le, Van-Hoang Zhang, Hongyu Xiao, Yi |
| Author_xml | – sequence: 1 givenname: Van-Hoang surname: Le fullname: Le, Van-Hoang email: hoang.le@newcastle.edu.au organization: The University of Newcastle,Australia – sequence: 2 givenname: Yi surname: Xiao fullname: Xiao, Yi email: yixiao@cqu.edu.cn organization: Chongqing University,China – sequence: 3 givenname: Hongyu surname: Zhang fullname: Zhang, Hongyu email: hyzhang@cqu.edu.cn organization: Chongqing University,China |
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| Snippet | Software-intensive systems often produce console logs for troubleshooting purposes. Log parsing, which aims at parsing a log message into a specific log... |
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| SubjectTerms | Business Chatbots Contrastive learning Large language models log analytics log parsing Optimization methods pre-trained LMs Scalability Semantics Software engineering |
| Title | Unleashing the True Potential of Semantic-Based Log Parsing with Pre-Trained Language Models |
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