Log Parsing: How Far Can ChatGPT Go?
This addresses the problem of automating log parsing for software engineers, but it is incremental as it applies an existing method (ChatGPT) to a new task.
The paper evaluated ChatGPT's performance on automated log parsing, a key step in software analytics, finding that it achieves promising results with appropriate prompts, particularly few-shot prompting.
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.