一句话：提出增量有界故障空间探索并识别最小失败集，为微服务韧性测试提供高效注入与 LLM 辅助诊断。
微服务架构已成为云原生应用的事实标准，但复杂的服务间依赖使其极易发生级联故障，因此通过注入故障来验证系统行为的韧性测试至关重要。本文提出 FaultWeave，一个实用且有效的微服务韧性测试框架，并具备故障诊断能力。基于小范围假设（small scope hypothesis），FaultWeave 设计了一种高效的故障空间探索技术，增量地探索有界深度内的故障组合，并充分利用先前的故障注入结果来加速测试执行、减少冗余测试场景。该增量策略能够自然地识别最小失败集（Minimal Failure Sets, MFS），即触发韧性失败的最小故障组合，从而为 LLM 辅助的故障诊断提供结构化的差分画像。该框架在中国某大型电网企业的企业级云原生应用（包含 512 个微服务）上进行了为期三个月的工业部署，共发现 237 个韧性缺陷。评估表明，相比现有的人工实践，该工作在测试效率和所发现的韧性故障方面均有显著提升。
Microservice architecture has become the de-facto standard for developing cloud-native applications, yet its complex inter-service dependencies make systems highly fragile to cascading failures. Resilience testing, which validates system behavior by injecting various faults, is therefore critical to improve the robustness of target systems. We present FaultWeave, a practical and effective resilience testing framework with failure diagnosis capability for microservice applications. Based on the small scope hypothesis, Fault-Weave designs an efficient fault space exploration technique which incrementally explores fault combinations up to a bounded depth, taking full advantage of previous fault injection results to speed up test execution and reduce redundant test scenarios. This incremental strategy naturally identifies Minimal Failure Sets (MFS)—the smallest fault combinations that trigger resilience failures—which provide structured differential profiles for LLM-assisted failure diagnosis. Industrial deployment on an enterprise-level cloud-native application (containing 512 microservices) at a large power grid company in China over three months discovered 237 resilience vulnerabilities. The evaluation demonstrates significant improvements in testing efficiency and discovered resilience failures compared to existing manual practices.
@inproceedings{ZhengWZWCZL26,
title = {FaultWeave: Bounded Resilience Testing with Failure Diagnosis Capability for Microservice Applications},
author = {Mingzhuo Zheng and Guoquan Wu and Jinbo Zhang and Jun Wei and Wei Chen and Jiaxin Zhu and Zheheng Liang},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805257}
}