一句话：提出 SpecRCA，用"先假设后验证"范式让 LLM 快速草拟候选根因并路径并行验证，提升微服务根因分析的准确性与效率。
微服务凭借资源弹性、松耦合架构与轻量部署成为云原生企业应用的骨干，但其内在复杂性与动态运行时交互不可避免地引发异常，因此保障系统可靠性依赖有效的根因分析（RCA）——既要定位异常来源，也要及时、可解释地刻画底层故障。近期基于大语言模型（LLM）的智能 RCA 方法减少了对人工特征的依赖，并具备跨平台适应性、任务泛化性与灵活性，但仍存在两个关键局限：探索多样性不足导致准确性受损，以及过度依赖大规模 LLM 造成推理缓慢。为此，本文提出 SpecRCA，一种面向微服务的推测式根因分析框架，采用"先假设后验证"范式：先由假设草拟模块快速生成候选根因，再由并行的根因验证器高效验证。在 AIOps 2022 数据集上的初步实验表明，SpecRCA 相比现有方法取得了更优的准确性与效率，显示出其作为复杂微服务环境中可扩展、可解释 RCA 实用方案的潜力。
Microservice systems have become the backbone of cloud-native enterprise applications due to their resource elasticity, loosely coupled architecture, and lightweight deployment. Yet, the intrinsic complexity and dynamic runtime interactions of such systems inevitably give rise to anomalies. Ensuring system reliability therefore hinges on effective root cause analysis (RCA), which entails not only localizing the source of anomalies but also characterizing the underlying failures in a timely and interpretable manner. Recent advances in intelligent RCA techniques, particularly those powered by large language models (LLMs), have demonstrated promising capabilities, as LLMs reduce reliance on handcrafted features while offering cross-platform adaptability, task generalization, and flexibility. However, existing LLM-based methods still suffer from two critical limitations: (a) limited exploration diversity, which undermines accuracy, and (b) heavy dependence on large-scale LLMs, which results in slow inference. To overcome these challenges, we propose SpecRCA, a speculative root cause analysis framework for microservices that adopts a hypothesize-then-verify paradigm. SpecRCA first leverages a hypothesis drafting module to rapidly generate candidate root causes, and then employs a parallel root cause verifier to efficiently validate them. Preliminary experiments on the AIOps 2022 dataset demonstrate that SpecRCA achieves superior accuracy and efficiency compared to existing approaches, highlighting its potential as a practical solution for scalable and interpretable RCA in complex microservice environments.
@inproceedings{paperbot12036,
title = {Hypothesize-Then-Verify: Speculative Root Cause Analysis for Microservices with Pathwise Parallelism},
author = {Lingzhe Zhang and Tong Jia and Yunpeng Zhai and Leyi Pan and Chiming Duan and Minghua He and Pei Xiao and Ying Li},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3786582.3786803}
}