Log Parsing: How Far Can ChatGPT Go?

TL;DR

This paper evaluates ChatGPT's effectiveness in automated log parsing, demonstrating promising results with proper prompting, especially few-shot methods, and discusses challenges and opportunities in this application.

Contribution

It is the first comprehensive assessment of ChatGPT's capabilities in log parsing, highlighting its potential and limitations in this domain.

Findings

ChatGPT can effectively parse logs with proper prompts.

Few-shot prompting improves log parsing performance.

Several challenges and opportunities are identified for ChatGPT-based log parsing.

Abstract

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.