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Automated log analysis is crucial in modern software-intensive systems forensuring reliability and resilience throughout software maintenance andengineering life cycles. Existing methods perform tasks such as log parsing andlog anomaly detection by providing a single prediction value withoutinterpretation. However, given the increasing volume of system events, thelimited interpretability of analysis results hinders analysts' trust and theirability to take appropriate actions. Moreover, these methods requiresubstantial in-domain training data, and their performance declines sharply (byup to 62.5%) in online scenarios involving unseen logs from new domains, acommon occurrence due to rapid software updates. In this paper, we proposeLogPrompt, a novel zero-shot and interpretable log analysis approach. LogPromptemploys large language models (LLMs) to perform zero-shot log analysis tasksvia a suite of advanced prompt strategies tailored for log tasks, whichenhances LLMs' performance by up to 107.5% compared with simple prompts.Experiments on nine publicly available evaluation datasets across two tasksdemonstrate that LogPrompt, despite using no training data, outperformsexisting approaches trained on thousands of logs by up to around 50%. We alsoconduct a human evaluation of LogPrompt's interpretability, with sixpractitioners possessing over 10 years of experience, who highly rated thegenerated content in terms of usefulness and readability (averagely 4.42/5).LogPrompt also exhibits remarkable compatibility with open-source andsmaller-scale LLMs, making it flexible for practical deployment.

LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis