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Yiran Li

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Preprint Aug 2026

ORCA: Observability-Grounded Program Repair for Microservice Incidents

Microservice failures are often diagnosed from operational telemetry. However, automated program repair systems usually start from issue reports, localized code context, or failing tests. This mismatch leaves a gap between telemetry-based diagnosis and patch generation. We present ORCA, an observability-grounded APR pipeline for microservice incidents. ORCA first distills the differences in paired failure and reference telemetry into a fault signature, then uses the signature to identify candidate code and deployment-configuration locations. Repair graph agents and an Exploration agent generate unified-diff patch candidates from these locations. ORCA evaluates generated patches with a Telemetry-Grounded Patch Verifier that separates patch validity, syntactic and semantic correctness, test-oracle integrity, and telemetry replay. On a 575-case benchmark, ORCA outperforms all evaluated baselines in terms of cost-effectiveness. Results show that operational telemetry can be transformed from diagnostic evidence into actionable repair context: paired telemetry supports repair-oriented localization, while repair graph agents convert localized code and configuration evidence into constrained patch-generation context for the LLM. Telemetry-grounded verification then exposes repair outcomes that issue- or test-only evaluation would miss.

Yuanchen Gao, Yifang Tian, Yiran Li et al. · 0 citations
Preprint Aug 2026

When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry

AGENTCHAOSBENCH is presented, a benchmark for detecting and localizing runtime faults in agentic systems from their execution telemetry, and its held-out labels and compact prediction format support reproducible comparison of LLM-based and non-LLM diagnosis methods.

Chenkai Zhang, Yiran Li, Yifang Tian et al. · 0 citations

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