integrate external apis

This work addresses the limitations of traditional workflow platforms, which rely on static, pre-defined processes and struggle to accommodate the dynamic data integration demands of distributed systems. To overcome this, the authors propose a configuration-driven runtime orchestration framework that dynamically constructs execution graphs at request time through dependency-aware scheduling and parallel task execution, thereby circumventing the constraints of fixed workflows. This approach enables rapid adaptation to evolving integration scenarios without requiring system redeployment, significantly reducing latency. Empirical evaluation in a real-world Customer 360 enterprise use case demonstrates that the framework offers substantial advantages in flexibility, scalability, and efficient data aggregation compared to conventional solutions.

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 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.

This work addresses the challenges of manual Web API integration testing, which is time-consuming, error-prone, and often misaligned with business requirements. The authors propose a novel approach that synergistically combines large language models (LLMs), retrieval-augmented generation (RAG), and prompt engineering to jointly parse natural language business requirements and OpenAPI specifications, thereby automatically generating executable test scripts that are both semantically meaningful and syntactically correct. Evaluated on ten real-world APIs, the method successfully produced valid tests for 89% of the business requirements within three attempts, uncovered multiple previously unknown integration defects, and substantially reduced the manual effort required for test development.

Third-party APIs are susceptible to regional outages, rate limiting, or quota exhaustion, often leading to user-visible service disruptions. This work proposes a configuration-driven dynamic API routing architecture that decouples routing policies from application logic, enabling runtime vendor switching without redeployment. The architecture formalizes a multidimensional factor model and integrates real-time telemetry, sliding-window health metrics, circuit breakers, bulkhead isolation, and a closed-loop decision engine to automate optimal routing based on performance indicators such as completion rate. Evaluated in an anonymous SMS verification scenario, the system successfully replaces manual intervention and significantly enhances service resilience and availability.

本文提出APIPilot框架，通过执行验证LLM推断的依赖关系并基于响应调整，生成有效的REST API测试序列，提高测试覆盖率和成功率。

This study addresses the limitation that analyzing prompts alone is insufficient for comprehensively evaluating developer interactions with AI programming agents. To overcome this, we propose a novel multidimensional interaction analysis framework termed "Say-Do-Understand," which integrates prompt data, screen activity, and comprehension metrics through a systematic five-stage end-to-end workflow. Employing an observational methodology, the analysis utilizes a prompt codebook, a screen activity coding scheme, and dual scoring rubrics. An empirical study involving ten experienced developers validates the proposed approach. Furthermore, four developer personas synthesizing task performance and comprehension levels are introduced to elucidate behavioral variations. Notably, the findings reveal that excessive reliance on agent self-checking significantly reduces developers' autonomous testing time.

This study addresses a critical gap in black-box testing research, which commonly assumes the correctness of OpenAPI specifications while overlooking how their defects impact testing efficacy. The authors propose the first taxonomy of OpenAPI faults, derived from literature, categorizing them into six types, and systematically inject these faults across five severity levels. Using EvoMaster, RESTler, and Schemathesis, they evaluate the effects on two microservice benchmarks through multidimensional metrics—including code and specification coverage, request/response quality, and behavioral diversity. Their findings reveal heterogeneous degradation patterns: method semantics–related faults cause comprehensive performance deterioration, whereas response code modifications have negligible impact. Notably, relaxing schema constraints substantially degrades request/response quality without affecting coverage metrics, demonstrating that reliance solely on coverage can obscure critical quality issues.

本文介绍了一种名为RESTCov的工具，它通过解析OpenAPI规范和HTTP请求/响应日志来分析REST API的结构覆盖情况，解决了传统方法难以应用于分布式API的问题。

研究解决了AI代理与工具间的工作流失败问题，通过提出一种效应历史模型和异常目录，并探讨了现有工具接口的局限性。