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Ensuring the reliability and availability of cloud services necessitatesefficient root cause analysis (RCA) for cloud incidents. Traditional RCAmethods, which rely on manual investigations of data sources such as logs andtraces, are often laborious, error-prone, and challenging for on-callengineers. In this paper, we introduce RCACopilot, an innovative on-call systemempowered by the large language model for automating RCA of cloud incidents.RCACopilot matches incoming incidents to corresponding incident handlers basedon their alert types, aggregates the critical runtime diagnostic information,predicts the incident's root cause category, and provides an explanatorynarrative. We evaluate RCACopilot using a real-world dataset consisting of ayear's worth of incidents from Microsoft. Our evaluation demonstrates thatRCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnosticinformation collection component of RCACopilot has been successfully in use atMicrosoft for over four years.

Automatic Root Cause Analysis via Large Language Models for Cloud Incidents