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.
Abstract
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.
Cloud computing acts as the core foundation supporting modern digital services, and hosts large-scale applications across five major domains: finance, healthcare, e-commerce, education, and industrial automation. However, its inherent complex, distributed, and dynamic native characteristics make it susceptible to four types of failures: sudden latency spikes, service outages, resource exhaustion, and security vulnerabilities. Traditional monitoring systems, which can only respond to failures after they occur, cannot guarantee the resilience of cloud environments. To address this issue, this paper proposes an integrated framework that combines chaos engineering and observability: chaos engineering injects controlled failures into production-like environments to evaluate a system’s load-bearing capacity, while observability obtains in-depth insights into a system through metrics, logs, distributed tracing, and event analysis. This framework unifies the capabilities of the two types of platforms to realize three core functions: proactive failure detection, automated recovery, and continuous resilience verification. We conducted validation experiments based on Kubernetes-powered containerized microservices, paired with Prometheus, Grafana, Jaeger, and LitmusChaos. Experimental results show that the framework achieves notable improvements across four dimensions: failure detection time, system recovery rate, service availability, and operational reliability. It can help all types of organizations identify hidden vulnerabilities, cut downtime, and strengthen service continuity.
Ganesh Gurudu, Ajay Devineni· 2026 International Conferenc...· 0 citations
The paper formalizes the underlying scenario-based evaluation model, details the design and implementation of the operator, and demonstrates its capabilities through a controlled benchmark experiment on the Online Boutique application, covering hundreds of automatically orchestrated evaluation scenarios across multiple workloads, failure levels, and retry configurations.
C. Aderaldo, N. C. Mendonça· Journal of Software Engineer...· 0 citations
The rapid adoption of cloud-native banking platforms and distributed fintech applications has significantly increased the use of stateful financial microservices for payment processing, transaction management, fraud detection, and digital financial operations. However, these distributed financial systems are very sensitive to runtime failures, cascading service failures, transaction inconsistencies, infrastructure instability, and operational risks due to latency under dynamic workloads. Traditional resilience testing and monitoring methods can fail to detect unknown weaknesses and critical failures in a real-time financial landscape. Based on these considerations, the present paper introduces a novel framework, dubbed AI-Driven Chaos Engineering (AI-CE), for enhancing the resilience and operational reliability of stateful financial microservices in cloud-native environments. The framework suggests that these capabilities, including AI-driven anomaly detection, distributed observability, adaptive fault orchestration, predictive failure analysis, and automated self-healing recovery, be centralized into a single resilience engineering architecture. Real-time telemetry data from transaction services, APIs, databases, message brokers, and containerized workloads are constantly monitored with AI-based behavior analytics to detect sensitive operational states and dynamically create intelligent chaos experiments such as latency injection, network partitioning, resource exhaustion, service crashes, and transaction interruption experiments. Experimental assessment shows that the proposed framework attains almost 96.32% classification accuracy and significantly enhances the fault detection capability, recovery efficiency, transaction consistency, service availability, and operational resilience over traditional chaos engineering methods. The proposed research helps in developing the smart, adaptive, and self-healing financial microservices ecosystems that will enable reliable next-generation digital banking and fintech infrastructures.
Rohith Venkata Sai Kumar Potladurthy, Mufaddal Munim, Rajesh Makala· 2026 International Conferenc...· 0 citations
Software fault injection assesses the resilience of complex systems, especially in cloud environments with multiple interdependent components. Since many software releases contain bugs not detected by traditional testing, this technique simulates realistic failures (e.g., by code mutation) to observe system behavior under defects, identify vulnerabilities before production, and understand how failures propagate. This paper presents CIMut, a fault injection tool through source code mutation in cloud environments, publicly available as a web application. The proposed approach was evaluated through an experimental study on the OpenStack cloud platform. A total of 62 experiments were performed on OpenStack, each injecting faults into different system components. The study showed that 96.7% of the injected faults resulted in bugs, classified as explicit errors (crashes, exceptions) or bugs with functional impact (incorrect behavior, data loss). These results demonstrate that the CIMut tool can generate representative faults that can be used to assess the resilience of complex software systems such as OpenStack.
Guilherme Silva, E. Sousa· Revista de Informática Teóri...· 0 citations
Fault injection is widely used to evaluate the resilience of microservice systems, where client requests often span multiple services and execution stages. Existing request-level techniques usually control where and what faults are injected, but not when they are activated within a distributed execution. This limitation makes it difficult to reproduce timing-dependent failures, such as failures after state-changing side effects, order-sensitive concurrent responses, and partial failures among repeated downstream calls. This paper presents SequenceFI, a non-intrusive framework for temporal fault injection in microservice systems. SequenceFI observes message-level send and receive events, propagates compact temporal evidence along request executions, and triggers faults only when occurrence-sensitive temporal guards are satisfied. It further synthesizes temporal guards from traces, reducing the need for exhaustive enumeration of temporal fault-injection configurations, while requiring no modifications to application code or serialization libraries. We implement SequenceFI on Kubernetes and evaluate it on four widely used microservice benchmarks. Across nine temporal-fault scenarios and 450 valid trials, SequenceFI achieves 100.0\% temporal success without premature or multiple injections, finds effective configurations in one attempt on average, and reduces aggregate end-to-end search time by 95.91\% compared with H-Random.
Yuzhen Tan, Jian Wang, Bing Li et al.· arXiv.org· 0 citations
A formal verification framework that consists of a combination of specifications of temporal logic, model checking and modeling of service interactions graphs to study the behavior of the system and confirm the correctness properties is suggested.
Aparna S. Patil, Pratibha V. Kashid, Sourabh Rungta et al.· Journal of Intelligent Decis...· 0 citations
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