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Log Parsing: How Far Can ChatGPT Go?
被引:3
|作者:
Le, Van-Hoang
[1
]
Zhang, Hongyu
[2
]
机构:
[1] Univ Newcastle, Sch Informat & Phys Sci, Callaghan, NSW, Australia
[2] Chongqing Univ, Sch Big Data & Software Engn, Chongqing, Peoples R China
基金:
澳大利亚研究理事会;
关键词:
Log analytics;
Log parsing;
Large language model;
ChatGPT;
D O I:
10.1109/ASE56229.2023.00206
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
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.
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页码:1699 / 1704
页数:6
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