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Automatic Root Cause Analysis via Large Language Models for Cloud Incidents

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arxiv 2305.15778 v4 pith:24EZN4DU submitted 2023-05-25 cs.SE

Yinfang Chen , Huaibing Xie , Minghua Ma , Yu Kang , Xin Gao , Liu Shi , Yunjie Cao , Xuedong Gao
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Hao Fan Ming Wen Jun Zeng Supriyo Ghosh Xuchao Zhang Chaoyun Zhang Qingwei Lin Saravan Rajmohan Dongmei Zhang Tianyin Xu
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classification cs.SE
keywords incidentsrcacopilotcloudcauserootanalysisdiagnosticincident
verification ladder T0 review T1 audit T2 compute T3 formal
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Ensuring the reliability and availability of cloud services necessitates efficient root cause analysis (RCA) for cloud incidents. Traditional RCA methods, which rely on manual investigations of data sources such as logs and traces, are often laborious, error-prone, and challenging for on-call engineers. In this paper, we introduce RCACopilot, an innovative on-call system empowered by the large language model for automating RCA of cloud incidents. RCACopilot matches incoming incidents to corresponding incident handlers based on their alert types, aggregates the critical runtime diagnostic information, predicts the incident's root cause category, and provides an explanatory narrative. We evaluate RCACopilot using a real-world dataset consisting of a year's worth of incidents from Microsoft. Our evaluation demonstrates that RCACopilot achieves RCA accuracy up to 0.766. Furthermore, the diagnostic information collection component of RCACopilot has been successfully in use at Microsoft for over four years.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning

    cs.LG 2026-04 conditional novelty 6.0 of 10

    OpsLLM's pipeline (HITL data curation, SFT, GRPO RL with a domain process reward model) improves LLM accuracy on software-operations QA and RCA, especially on in-distribution root-cause-analysis tasks.

  2. Auditable Graph-Guided Root Cause Analysis for Kubernetes Incidents

    cs.SE 2026-06 conditional novelty 5.0 of 10

    Graph Traversal Agent improves root-cause F1 from 0.6087 to 0.9130 on ITBench snapshots but the gain is benchmark-coupled to cases where the injected fault is already in the evidence graph.

  3. OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning

    cs.LG 2026-04 unverdicted novelty 5.0 of 10

    OpsLLM outperforms general LLMs on software operations QA and RCA tasks through human-in-the-loop data curation, supervised fine-tuning, and domain-specific reinforcement learning.

  4. OpsLLM: Construction of Large Language Model for Software Operations with Multi-stage Learning

    cs.LG 2026-04 unverdicted novelty 4.0 of 10

    OpsLLM is a domain-specific LLM for software ops QA and RCA built with human-curated data, SFT, and RL using a domain process reward model, showing accuracy gains of 0.2-5.7% on QA and 2.7-70.3% on RCA over general LLMs.

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