- Title
- Log Parsing: How Far Can ChatGPT Go?
- Creator
- Le, Van-Hoang; Zhang, Hongyu
- Relation
- 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). Proceedings of 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (Luxembourg, Echternach 11-15 Spetember, 2023) p. 1699-1704
- Relation
- ARC.DP200102940 http://purl.org/au-research/grants/arc/DP200102940
- Publisher Link
- http://dx.doi.org/10.1109/ASE56229.2023.00206
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2023
- Description
- Software logs play an essential role in ensuring the reliability and maintainability of large-scale software systems, as they are often the sole source of runtime information. Log parsing, which converts raw log messages into structured data, is an important initial step towards downstream log analytics. In recent studies, ChatGPT, the current cutting-edge large language model (LLM), has been widely applied to a wide range of software engineering tasks. However, its performance in automated log parsing remains unclear. In this paper, we evaluate ChatGPT's ability to undertake log parsing by addressing two research questions. (1) Can ChatGPT effectively parse logs? (2) How does ChatGPT perform with different prompting methods? Our results show that ChatGPT can achieve promising results for log parsing with appropriate prompts, especially with few-shot prompting. Based on our findings, we outline several challenges and opportunities for ChatGPT-based log parsing.
- Subject
- log analytics; log parsing; large language model; ChatGPT
- Identifier
- http://hdl.handle.net/1959.13/1502484
- Identifier
- uon:55231
- Language
- eng
- Reviewed
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