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Towards Extracting Web API Specifications from Documentation

机译:从文档中提取Web API规范

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Web API specifications are machine-readable descriptions of APIs. These specifications, in combination with related tooling, simplify and support the consumption of APIs. However, despite the increased distribution of web APIs, specifications are rare and their creation and maintenance heavily rely on manual efforts by third parties. In this paper, we propose an automatic approach and an associated tool called D2Spec for extracting significant parts of such specifications from web API documentation pages. Given a seed online documentation page of an API, D2Spec first crawls all documentation pages on the API, and then uses a set of machine-learning techniques to extract the base URL, path templates, and HTTP methods - collectively describing the endpoints of the API. We evaluate whether D2Spec can accurately extract endpoints from documentation on 116 web APIs. The results show that D2Spec achieves a precision of 87.1% in identifying base URLs, a precision of 80.3% and a recall of 80.9% in generating path templates, and a precision of 83.8% and a recall of 77.2% in extracting HTTP methods. In addition, in an evaluation on 64 APIs with pre-existing API specifications, D2Spec revealed many inconsistencies between web API documentation and their corresponding publicly available specifications. API consumers would benefit from D2Spec pointing them to, and allowing them thus to fix, such inconsistencies.
机译:Web API规范是API的机器可读描述。这些规范与相关工具相结合,简化并支持API的使用。但是,尽管Web API的分布有所增加,但规范却很少见,其创建和维护很大程度上依赖于第三方的手动工作。在本文中,我们提出了一种自动方法和一个称为D2Spec的关联工具,用于从Web API文档页面提取此类规范的重要部分。给定API的原始在线文档页面,D2Spec首先会爬网API上的所有文档页面,然后使用一套机器学习技术来提取基本URL,路径模板和HTTP方法-共同描述API的端点。我们评估D2Spec是否可以从116个Web API的文档中准确提取端点。结果表明,D2Spec识别基本URL的精度为87.1%,生成路径模板的精度为80.3%,召回率为80.9%,提取HTTP方法的精度为83.8%,召回率为77.2%。此外,在对64种具有预先存在的API规范的API进行评估时,D2Spec揭示了Web API文档与其相应的公开可用规范之间存在许多不一致之处。 API使用者可以从D2Spec指向他们的方法中受益,并允许他们解决此类不一致问题。

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