Fault injection is a key technique for assessing software reliability, enabling proactive detection of system defects before they manifest in production. However, the increasing complexity of microservice architectures leads to exponential growth in the fault-injection space, rendering traditional random injection inefficient. Recent lineage-driven approaches mitigate this problem through heuristic pruning, but they face two limitations. First, combinatorial-fault discovery remains bottlenecked by general-purpose SAT solvers, which fail to exploit the monotone and low-overlap structure of derived CNF formulas and typically rely on a static upper bound on fault size. Second, existing techniques provide limited post-injection guidance beyond reporting detected faults. To address these challenges, we propose FastFI, a fault-injection-guided framework to enhance the robustness of API call sites in microservice-based systems. FastFI features a DFS-based solver with dynamic fault injection to discover all valid combinatorial faults, and it leverages fault-injection results to identify critical APIs whose call sites should be hardened for robustness. Experiments on four representative microservice benchmarks show that FastFI reduces end-to-end fault-injection time by an average of 76.12\% compared to state-of-the-art baselines while maintaining acceptable resource overhead. Moreover, FastFI accurately identifies high-impact APIs and provides actionable guidance for call-site hardening.

翻译：故障注入是评估软件可靠性的关键技术，能够在系统缺陷在生产环境中显现之前主动检测。然而，微服务架构日益增长的复杂性导致故障注入空间呈指数级增长，使得传统的随机注入方法效率低下。近期的谱系驱动方法通过启发式剪枝缓解了这一问题，但仍面临两个局限。首先，组合故障的发现受限于通用SAT求解器，这些求解器未能利用派生CNF公式的单调性与低重叠结构，且通常依赖故障规模的静态上界。其次，现有技术在报告检测到的故障之外，提供的注入后指导有限。为应对这些挑战，我们提出FastFI，一个基于故障注入的框架，旨在增强微服务系统中API调用点的鲁棒性。FastFI采用基于DFS的求解器结合动态故障注入来发现所有有效的组合故障，并利用故障注入结果识别出关键API，其调用点需进行强化以提升鲁棒性。在四个代表性微服务基准测试上的实验表明，与最先进的基线方法相比，FastFI平均将端到端故障注入时间降低了76.12%，同时保持了可接受的资源开销。此外，FastFI能准确识别高影响API，并为调用点强化提供可操作的指导。