ORCA: Observability-Grounded Program Repair for Microservice Incidents

2026-08-17

AI Summary

ORCA通过基于观测性的方法，将微服务故障诊断与程序修复相结合，利用故障特征定位问题代码并生成修复补丁，有效解决了从诊断到修复的转换难题。

Abstract

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.

Problem

• microservice failures
• operational telemetry
• automated program repair
• diagnosis and patch generation
• mismatch

Innovation

• Observability-Grounded
• Fault Signature
• Repair Graph Agents
• Telemetry-Grounded Patch Verifier