integration

Embedded systems face significant challenges in hardware-software co-development, including strong hardware dependencies, stringent real-time and safety requirements, and poor compatibility with conventional CI/CD practices. Method: Through a systematic literature review of 20 academic and industrial studies, we establish the first DevOps practice taxonomy specifically for embedded systems; propose a hardware-aware CI/CD framework supporting closed-loop hardware testing, resource-constrained execution, and safety compliance; and identify and address critical gaps in deployment automation and observability. Contribution/Results: We synthesize toolchain design, automated testing strategies, pipeline lightweighting, and firmware security practices into a structured knowledge framework. This work provides both a theoretical foundation and concrete research directions for academia, and delivers a reusable, industry-applicable methodology for realizing Embedded DevOps.

Current RESTful API design quality assessment relies heavily on manual inspection, lacking early, automated validation mechanisms for non-functional requirements—particularly interoperability, modularity, and maintainability. Method: This paper proposes an OpenAPI-based static analysis approach that implements a configurable rule engine. It formalizes 75 design principles derived from scholarly literature and industry standards into structured, machine-checkable constraints, enabling customizable rule activation/deactivation and traceable feedback to align requirements engineering with architectural governance. Contribution/Results: Following the design science research paradigm, we developed and evaluated a prototype tool. Empirical evaluation and expert review demonstrate that the method significantly improves API design compliance and consistency, achieving 82% automation coverage. It effectively supports continuous architectural governance in agile development environments, bridging the gap between design-time assurance and operational API lifecycle management.

This work addresses the oracle problem in REST API testing—stemming from the absence of explicit expected outputs—by proposing a novel multi-agent workflow grounded in large language models (LLMs). The approach uniquely integrates multi-agent LLMs with metamorphic testing to automatically parse OpenAPI specifications and generate executable test cases in the Given-When-Then format, eliminating the need for manually defined oracles. Evaluated on two public web applications, the method demonstrates its effectiveness by uncovering API behaviors missed by existing scenario-based testing techniques and substantially enhancing test coverage. This study thus introduces a new paradigm for automated API testing that leverages the reasoning and coordination capabilities of LLM-based agents to overcome longstanding challenges in test oracle specification.

This work addresses interoperability challenges in distributed systems arising from heterogeneous services, multi-version REST APIs, GraphQL endpoints, and IoT devices due to data schema mismatches. The authors propose a FastAPI-based runtime middleware that, for the first time, shifts Bass et al.’s interoperability tactics from design time to runtime. Leveraging large language models (LLMs), the approach dynamically detects structural and semantic discrepancies and implements a dual-path transformation strategy—either generating reusable adapter code or performing on-the-fly request-level conversion—through a five-stage pipeline. It also incorporates a three-tier security mechanism comprising validation, ensemble voting, and rule-based fallback. Evaluated across ten scenarios, the best configuration achieves a pass@1 accuracy of 0.90, with the CODEGEN strategy (mean 0.83) significantly outperforming DIRECT (0.77); notably, the highest-accuracy models also exhibit the lowest inference costs.

This work addresses the lack of effective automated testing mechanisms in microservice architectures, where existing API specifications such as OpenAPI suffer from limited semantic expressiveness and thus struggle to support high-coverage automated validation. To overcome this limitation, the authors propose APOSTL—an extension of OpenAPI grounded in restricted first-order logic—that enables formal annotation of semantic properties of APIs. Complementing this specification language, they develop PETIT, a tool that performs fully automated, source-code-free black-box testing using only APOSTL-annotated OpenAPI documents. By embedding formal logic directly into API specifications for the first time, this approach allows interface documentation to drive semantically precise and high-coverage automated tests, significantly enhancing the efficiency and reliability of microservice verification.

Software engineers face significant challenges—including difficulty in modeling, lengthy prototyping cycles, and high verification costs—when developing control algorithms for complex dynamic systems such as communication networks. To address these issues, we propose GIPS, the first model-driven engineering framework that tightly integrates graph-structured integer linear programming (ILP) modeling with automated code generation. Using the domain-specific language GIPSL, users declaratively specify constraints and optimization objectives; GIPS then automatically generates functionally complete, executable Java graph-optimization components. This enables end-to-end rapid prototyping—from high-level specifications to runtime deployment. We validate GIPS on a tree-structured peer-to-peer topology control scenario, demonstrating its correctness, efficiency, and scalability. The full implementation—including source code and a ready-to-run virtual machine demonstration environment—is open-sourced, confirming its practical deployability and engineering utility.

This work addresses the fragility, inefficiency, and strong platform coupling commonly found in CI/CD pipelines for legacy COBOL systems, which often result in high maintenance costs and vendor lock-in. To overcome these challenges, the authors propose a portable CI/CD architecture tailored for highly secure and compliance-driven environments. The approach leverages OCI-compliant container images preloaded with COBOL toolchains, introduces a platform abstraction layer, integrates multiple repositories, and employs Groovy script refactoring to achieve platform-agnostic continuous integration and delivery. Empirical evaluation demonstrates that the proposed solution significantly enhances efficiency—reducing pipeline execution time by 82%—while simultaneously improving system portability, security, and maintainability. This architecture offers a reusable paradigm for modernizing legacy COBOL applications within regulated domains.

To address the reduced service reusability and constrained energy efficiency caused by early binding of cloud design patterns in data mesh architectures, this paper proposes a non-intrusive, late-binding cloud pattern integration framework. The framework enables on-demand, dynamic injection of cloud design patterns—including circuit breakers, retries, and rate limiting—at deployment or runtime without modifying service source code, thereby preserving high reusability while optimizing energy consumption. Built on Kubernetes, it supports containerized orchestration, automated pattern injection, fine-grained runtime energy monitoring, multi-pipeline coordinated deployment, and adaptive decision-making. Experimental evaluation demonstrates that the framework improves service reuse rate by 32% while reducing average energy consumption by 19.7%, significantly enhancing both energy awareness and architectural flexibility of data-sharing pipelines.

This study addresses the proliferation of functional redundancy in service-oriented architectures caused by heterogeneous clients, which undermines system evolvability and maintainability. To mitigate this issue, the authors propose a novel reference architecture that synergistically integrates metadata-driven mechanisms with pattern languages. By leveraging metadata management and a plugin-based design, the approach effectively constrains service redundancy while enhancing reuse capabilities. The work innovatively combines metadata mechanisms and pattern languages in architectural construction and validates its efficacy through a triangulated evaluation method incorporating scenario-based assessment and real-world case studies. Empirical results demonstrate that the majority of system changes during evolution require no code modifications—only configuration adjustments or the addition of pluggable components—thereby significantly improving architectural stability and reuse efficiency.

This work addresses the challenges in edge and embedded application development—namely, heterogeneous software stacks, multi-language runtimes, and difficult debugging—which lead to rigid deployment workflows and complex fault diagnosis. To overcome these limitations, the paper proposes a novel architecture enabling unified end-edge-cloud development. Its core components include a single programming language, a retargetable runtime system, a local recording and replay mechanism for distributed events, and a cross-platform deployment framework. This design breaks down traditional debugging barriers in edge–cloud collaborative development, facilitating seamless scalability, consistent testing, and flexible deployment across heterogeneous environments. Evaluation of the prototype system demonstrates that the proposed approach significantly simplifies deployment procedures and enhances fault diagnosis efficiency.

This work addresses the limited applicability of large language models in safety-critical automotive systems engineering due to concerns regarding trustworthiness, traceability, and compatibility with established verification workflows. The authors propose workflow-level design principles for trustworthy generative AI and implement them within an end-to-end automotive engineering pipeline encompassing requirement change identification, SysML v2 architecture updates, and regression testing. To enhance completeness in change detection, they employ segmented prompt decomposition, diversity sampling, and lightweight NLP-based validation. Traceable test generation is achieved through explicit variable-to-port mappings. Experimental results demonstrate that the approach significantly improves the detection rate of critical changes in large-scale specifications, ensures correctness of architectural updates, and enables automated, traceable regression testing—providing a practical foundation for deploying generative AI in safety-critical contexts.