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RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data

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arxiv 2412.17015 v5 pith:G6OM2EER submitted 2024-12-22 cs.SE

classification cs.SE
keywords microservicesystemsbenchmarkanalysiscomprehensivedatasetsevaluationcause
verification ladder T0 review T1 audit T2 compute T3 formal
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Root cause analysis (RCA) for microservice systems has gained significant attention in recent years. However, there is still no standard benchmark that includes large-scale datasets and supports comprehensive evaluation environments. In this paper, we introduce RCAEval, an open-source benchmark that provides datasets and an evaluation environment for RCA in microservice systems. First, we introduce three comprehensive datasets comprising 735 failure cases collected from three microservice systems, covering various fault types observed in real-world failures. Second, we present a comprehensive evaluation framework that includes fifteen reproducible baselines covering a wide range of RCA approaches, with the ability to evaluate both coarse-grained and fine-grained RCA. We hope that this ready-to-use benchmark will enable researchers and practitioners to conduct extensive analysis and pave the way for robust new solutions for RCA of microservice systems.

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Cited by 3 Pith papers

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

  1. Safe Remediation as Risk-Constrained Intervention Decision in Microservice Systems

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A risk-constrained CMDP with a 3D risk filter and adaptive escalation gate is reported to reduce false remediation by 39% while improving success by 2.5 points over a runbook baseline on a microservice benchmark.

  2. TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A new 530-scenario benchmark for telecom alarm root cause analysis, plus an iterative agent that lifts F1 from 58.99% to 91.79% by repeatedly repairing its code against the benchmark.

  3. Autonomic Microservice Management via Agentic AI and MAPE-K Integration

    cs.SE 2025-06 conditional novelty 4.0 of 10

    A conceptual framework integrating MAPE-K with agentic AI for autonomous microservice anomaly management, including a proposed autonomic threshold for human oversight, offered without empirical validation.

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