Identifier lexicon may have a direct impact on software understandability and reusability and, thus, on the quality of the final software product. Understandability and reusability are two important characteristics of software quality. REpresentational State Transfer (REST) style is becoming a de facto standard adopted by software organizations to build their Web applications. Understandable and reusable Uniform Resource Identifers (URIs) are important to attract client developers of RESTful APIs because good URIs support the client developers to understand and reuse the APIs. Consequently, the use of proper lexicon in RESTful APIs has also a direct impact on the quality of Web applications that integrate these APIs. Linguistic antipatterns represent poor practices in the naming, documentation, and choice of identifiers in the APIs as opposed to linguistic patterns that represent the corresponding best practices. In this paper, we present the Semantic Analysis of RESTful APIs (SARA) approach that employs both syntactic and semantic analyses for the detection of linguistic patterns and antipatterns in RESTful APIs. We provide detailed definitions of 12 linguistic patterns and antipatterns and define and apply their detection algorithms on 18 widely-used RESTful APIs, including Facebook, Twitter, and Dropbox. Our detection results show that linguistic patterns and antipatterns do occur in major RESTful APIs in particular in the form of poor documentation practices. Those results also show that SARA can detect linguistic patterns and antipatterns with higher accuracy compared to its state-of-the-art approach — DOLAR.

中文翻译：

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用于检测语言模式和反模式的 RESTful API 语义分析

标识符词典可能对软件的可理解性和可重用性产生直接影响，从而对最终软件产品的质量产生影响。可理解性和可重用性是软件质量的两个重要特征。REpresentational State Transfer (REST) 风格正在成为软件组织用来构建其 Web 应用程序的事实标准。可理解和可重用的统一资源标识符 (URI) 对于吸引 RESTful API 的客户端开发人员很重要，因为好的 URI 支持客户端开发人员理解和重用 API。因此，在 RESTful API 中使用适当的词典也会直接影响集成这些 API 的 Web 应用程序的质量。语言反模式代表了命名、文档、以及 API 中标识符的选择，而不是代表相应最佳实践的语言模式。在本文中，我们介绍了 RESTful API 的语义分析 (SARA) 方法，该方法采用句法和语义分析来检测 RESTful API 中的语言模式和反模式。我们提供了 12 种语言模式和反模式的详细定义，并在 18 种广泛使用的 RESTful API（包括 Facebook、Twitter 和 Dropbox）上定义和应用了它们的检测算法。我们的检测结果表明，语言模式和反模式确实出现在主要的 RESTful API 中，特别是以不良文档实践的形式出现。这些结果还表明，与最先进的方法 DOLAR 相比，SARA 可以更准确地检测语言模式和反模式。