Jul 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 12 references
TL;DR
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.
Abstract
Microservices that are cloud-native allow distributed systems to scale, but present problems in assuring correctness, reliability, and fault resilience because of dynamic scaling and the complexity of service interactions. The presented paper suggests 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. Service states, dependencies and scaling dynamics are modeled in a mathematical system. The framework identifies deadlocks, provides liveness and safety, and a framework of fault propagation between services. Experimental analysis shows that the accuracy of deadlock detection is increased to 96.7% and fault detection to 95.4% with 94.6% verification coverage and time of 96 ms to detect faults is minimized as compared to the conventional testing and monitoring methods in dynamic microservice settings.
Microservice-based systems are modular and adaptable, but their distributed structure makes their timing behavior difficult to analyze and guarantee. Because latency emerges from interactions among service dependencies, shared resources, and coordinating middleware, local timing disturbances can propagate into system-level effects. This survey examines the sources, propagation mechanisms, and observable impacts of timing variability in microservice-based systems, using software-defined vehicles as a motivating example. It compares major classes of timing models by their assumptions about workload stability, execution structure, resource sharing, observability, and guarantee admissibility, and summarizes their limitations within microservice-based software-defined vehicles.
Cyrus K. Vattes, H. Mostafaei, N. Meratnia· 0 citations
Microservice-based systems evolve continuously through API changes, service splits and merges, dependency churn, and deployment-topology drift driven by DevOps practices. This rapid evolution erodes the assumptions encoded in formal models, test suites, and architectural descriptions, leaving practitioners with limited guidance on how to keep verification artifacts aligned with evolving implementations. Prior surveys cover microservices design, deployment, performance, and isolated verification techniques, but, to our knowledge, none consolidate the intersection of evolution and formal/rigorous verification, nor map how repository-derived signals and CI/CD pipelines support continuous verification of microservice-based systems. This paper addresses that gap through a systematic literature review (SLR). Following the Kitchenham et al. guidelines and PRISMA-style reporting, we defined a review protocol with four research questions covering (i) formal modeling and verification approaches, (ii) the impact of architectural and API evolution on verifiability, (iii) repository-derived signals for updating formal artifacts, and (iv) continuous and incremental verification in CI/CD pipelines. Structured searches in IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus returned 1187 records, which were screened in seven stages and reduced to 18 included studies (13 primary studies that supply evidence and five background studies retained for transparency and framing) through inclusion/exclusion criteria and a weighted 15-item quality-assessment instrument. We synthesized the included studies thematically and compared them along five dimensions: verification technique, evolution dimension, automation level, empirical evidence, and tool/artifact availability. The synthesis shows that actor-based model checking, TLA+ control-plane verification, static code analysis, continuous certification, and runtime/self-adaptive testing form five complementary clusters, but empirical evaluation is dominated by small academic case studies, only a minority of approaches automate model updates from repository signals, and few are integrated end-to-end into CI/CD. We discuss methodological limitations of the body of work, including a lack of industrial-scale validation and shared datasets, contrast intrinsic (build-time, model-driven) and extrinsic (recovery-based) verification strategies, and outline a research agenda toward repository-aware, pipeline-integrated verification for evolving microservices. The present synthesis distils a set of immediate, evidence-based actions for practitioners together with a focused agenda of open research challenges.
R. Gómez, Ebeid Elsayed, Enrique R. Zarate et al.· Software· 0 citations
Microservice architecture, with distributed deployment and loose coupling, is widely used in networked service systems. As the number of service modules grows and cross-service interactions become complex, formal modeling and verification face challenges. Hierarchical Colored Petri Net (HCPN)-based model checking can verify full execution paths, but manual modeling is costly, error-prone, and prone to state-space explosion. This paper proposes an automated HCPN modeling method that transforms Netflix Conductor workflow specifications into hierarchical HCPN models using predefined rules. The method preserves workflow structure, control flow, and data dependencies, and generates models suitable for formal verification. Experimental results show that the generated HCPN models are consistent with the original workflows and can automatically detect errors such as data inconsistency and business-data state inconsistency. Compared with existing methods, this approach reduces manual effort, lowers modeling cost, and provides more comprehensive support for data representation and error detection.
Guoshuai Li, Tao Sun, Wenjie Zhong· Proceedings of the ACM SIGCO...· 0 citations
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.
This paper explores the integration of Spring Boot with containerization and observability, focusing on best practices, architectural patterns, and challenges, and delves into containerization strategies using Docker and orchestration with Kubernetes, emphasizing their roles in deployment and scalability.
Youssef- Al-Habib, Omar Sullaiman· International Journal of Art...· 1 citation