一句话：提出模型发现与图模拟方法，自动从运行数据合成依赖图，为混沌工程提供低风险、可 CI/CD 化的轻量入口。
混沌工程能揭示韧性风险，但广泛频繁运行成本高、风险大；基于模型的分析常依赖手工建模且难以维护。本文提出模型发现：在 CI/CD 或可观测性平台中，自动从分布式追踪、服务网格遥测、配置与清单合成可分析的拓扑模型，并用图模拟估计可用性。在 DeathStarBench Social Network 上，从 Jaeger 提取依赖图，跨两种部署模式与五种故障率估计可用性。结果发现模型能紧密跟踪真实故障注入：有副本时中段故障率的中位符号误差近零，无副本时偏差与未建模机制一致。据此提出混合工作流：模型实时监测与筛选场景，昂贵实测留给关键风险。
Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim simple topological models provide fast, low-risk availability estimates. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests)—providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median signed error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results suggest a hybrid workflow: using models to monitor resilience in real-time and filter scenarios, while reserving expensive live validation for critical risks.