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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。这些规范与相关的工具组合,简化和支持API的消耗。但是,尽管Web API的分布增加,但规格很少见,他们的创建和维护大量依赖于第三方的手工努力。在本文中,我们提出了一种自动方法和称为D2Spec的相关工具,用于从Web API文档页面中提取这些规格的重要部分。给定API的种子在线文档页面,D2Spec首先爬上API上的所有文档页面,然后使用一组机器学习技术来提取基本URL,路径模板和HTTP方法 - 集体描述API的端点。我们评估D2Spec是否可以在116 Web API上的文档中准确提取端点。结果表明,D2SPEC在识别基本URL时达到87.1%的精度,精度为80.3%,并在发电路径模板时召回80.9%,提取HTTP方法的精度为83.8%,召回为77.2%。此外,在具有预先存在的API规范的64个API的评估中,D2Spec在Web API文档和相应的公开可用规范之间揭示了许多不一致。 API消费者将从D2SPEC中受益指向它们,从而允许它们来修复此类不一致。

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