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.
RCAEval: A Benchmark for Root Cause Analysis of Microservice Systems with Telemetry Data
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abstract
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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TN-AutoRCA: Benchmark Construction and Agentic Framework for Self-Improving Alarm-Based Root Cause Analysis in Telecommunication Networks
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.