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 \textit{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.

翻译：微服务系统凭借其资源弹性、松耦合架构和轻量级部署优势，已成为云原生企业应用的支柱。然而，此类系统固有的复杂性和动态运行时交互不可避免地会引发异常。确保系统可靠性因而取决于有效的根因分析（RCA），这不仅需要定位异常源，还需以及时且可解释的方式表征底层故障。智能RCA技术的最新进展，特别是基于大语言模型（LLMs）的方法，已展现出显著潜力：LLMs降低了对人工特征工程的依赖，同时提供了跨平台适应性、任务泛化能力和灵活性。然而，现有基于LLM的方法仍存在两个关键局限：（a）探索多样性有限，影响分析准确性；（b）对大规模LLMs的重度依赖导致推理速度缓慢。为克服这些挑战，我们提出SpecRCA——一种采用“假设后验证”范式的微服务推测性根因分析框架。SpecRCA首先利用假设生成模块快速产生候选根因，随后通过并行根因验证器高效验证这些假设。在AIOps 2022数据集上的初步实验表明，相较于现有方法，SpecRCA在准确性和效率方面均表现优异，凸显了其作为复杂微服务环境中可扩展、可解释RCA实用解决方案的潜力。