The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Log Parsing with Prompt-based Few-shot Learning
Open accessAbstract
Logs generated by large-scale software systems provide crucial information for engineers to understand the system status and diagnose problems of the systems. Log parsing, which converts raw log messages into structured data, is the first step to enabling automated log analytics. Existing log parsers extract the common part as log templates using statistical features. However, these log parsers often fail to identify the correct templates and parameters because: 1) they often overlook the semantic meaning of log messages, and 2) they require domain-specific knowledge for different log datasets. To address the limitations of existing methods, in this paper, we propose LogPPT to capture the patterns of templates using prompt-based few-shot learning. LogPPT utilises a novel prompt tuning method to recognise keywords and parameters based on a few labelled log data. In addition, an adaptive random sampling algorithm is designed to select a small yet diverse training set. We have conducted extensive experiments on 16 public log datasets. The experimental results show that LogPPT is effective and efficient for log parsing.
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“LogPPT[37] reconstructs log parsing into a promptbased few-shot learning task, accurately extracting templates and parameters with minimal annotations.”
From From Correlation to Intervention: A Survey on Log-Based Causal Analysis for Security and AIOps (2026) · cites this paper as [37]
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Cites
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“Hard prompt templates usually correspond to natural language phrases [41], in which each token in prompt templates is meaningful and understandable.”
BackgroundPrefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.353 · Cited by 2,473
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Research works printed but not linked: 8
- 2022, Artifact for “guidelines for assessing the accuracy of log message template identification techniques”
- 10.18653/v1/P16-1162 · DOI · Paper page
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- 2022, A toolkit for automated log parsing
- radford, 2018, Improving language understanding by generative pre-training
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- 2022, HDFS dataset
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Metadata
- Authors
- Le, V.-H., & Zhang, H.
- Affiliations
- University of Newcastle Australia (AU)
- Chongqing University (CN)
- Venue
- 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
- Publisher
- IEEE
- Published
- 2023-05
- Pages
- 2438-2449
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W4384345672
- Cited by
- 129
- References
- 56
- Citations by year
- 2026: 40 · 2025: 51 · 2024: 30 · 2023: 8
- FWCI
- 38.92
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- NOVA (University of Newcastle Australia) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- Australian Research Council (DP200102940,DP220103044)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Keywords
- log parsing, log templates, prompt tuning, log analysis, automated log analysis, training data selection
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