The number of Web APIs for accessing information and services is continuously increasing, and yet, no tools exist to automate the time-consuming and error-prone process of invoking those APIs and composing their responses. The recent emergence of widely-adopted, standardized, Web-API description formats and the development of Linked Data technologies for data integration have motivated our work on the LRA (Linked REST APIs) methodology [1, 2]. LRA relies on RDF service specifications to automate the development process around the usage of Web APIs. This automation represents a great opportunity to systematize and improve the quality of service-oriented application development. However, LRA’s reliance on SPARQL as the user-interaction model may hinder its adoption, because it requires developers to learn the intricacies of the unconventional graph data model and its associated datasets. In this paper we have developed the LRA Workbench (\(LRA_{Wbench}\)), which takes advantage of the emergent schema of Web-API specifications, in order to simplify the formulation of LRA-compliant SPARQL queries. Our empirical evaluation of the \(LRA_{Wbench}\) usability demonstrates that our tool significantly improves the performance of developers formulating SPARQL queries for LRA. A subsequent study on the effectiveness of the \(LRA_{Wbench}\) demonstrated that developers using LRA tend to produce code with considerable better structural complexity, in less time, than developers manually composing APIs.

用于访问信息和服务的 Web API 的数量在不断增加，但目前还没有任何工具可以自动执行调用这些 API 并编写其响应的耗时且容易出错的过程。最近出现的广泛采用的标准化 Web-API 描述格式以及用于数据集成的关联数据技术的发展，激发了我们在 LRA（关联 REST API）方法方面的工作 [1, 2]。LRA 依靠 RDF 服务规范来自动化围绕 Web API 使用的开发过程。这种自动化代表了系统化和提高面向服务的应用程序开发质量的绝佳机会。然而，LRA 依赖 SPARQL 作为用户交互模型可能会阻碍其采用，因为它要求开发人员了解非常规图形数据模型及其相关数据集的复杂性。在本文中，我们开发了 LRA 工作台（\(LRA_{Wbench}\) )，它利用了 Web-API 规范的紧急模式，以简化符合 LRA 的 SPARQL 查询的制定。我们对\(LRA_{Wbench}\) 可用性的实证评估表明，我们的工具显着提高了开发人员为 LRA 制定 SPARQL 查询的性能。对\(LRA_{Wbench}\)有效性的后续研究 表明，与手动编写 API 的开发人员相比，使用 LRA 的开发人员倾向于在更短的时间内生成结构复杂度更高的代码。