  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.
