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Improving API Caveats Accessibility by Mining API Caveats Knowledge Graph

机译：通过挖掘API Capeats知识图来改善API警告可访问性

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摘要

API documentation provides important knowledge about the functionality and usage of APIs. In this paper, we focus on API caveats that developers should be aware of in order to avoid unintended use of an API. Our formative study of Stack Overflow questions suggests that API caveats are often scattered in multiple API documents, and are buried in lengthy textual descriptions. These characteristics make the API caveats less discoverable. When developers fail to notice API caveats, it is very likely to cause some unexpected programming errors. In this paper, we propose natural language processing(NLP) techniques to extract ten subcategories of API caveat sentences from API documentation and link these sentences to API entities in an API caveats knowledge graph. The API caveats knowledge graph can support information retrieval based or entity-centric search of API caveats. As a proof-of-concept, we construct an API caveats knowledge graph for Android APIs from the API documentation on the Android Developers website. We study the abundance of different subcategories of API caveats and use a sampling method to manually evaluate the quality of the API caveats knowledge graph. We also conduct a user study to validate whether and how the API caveats knowledge graph may improve the accessibility of API caveats in API documentation.
机译：API文档为API的功能和使用提供了重要的知识。在本文中，我们专注于开发人员应该意识到的API警告，以避免意外使用API​​。我们对堆栈溢出问题的形成研究表明，API警告通常分散在多个API文件中，并埋在冗长的文本描述中。这些特性使API警告不太可发现。当开发人员未能注意到API警告时，很可能导致一些意外的编程错误。在本文中，我们提出了自然语言处理（NLP）技术从API文档中提取了API警告句子的十个子类别，并将这些句子链接到API警告知识图中的API实体。 API警告知识图可以支持基于信息检索的信息或以实体为中心搜索API警告。作为概念验证，我们为Android开发人员网站上的API文档构建API Capeats知识图表。我们研究API警告的不同子类别，并使用采样方法手动评估API警告知识图的质量。我们还进行用户学习，以验证API Capeats知识图是否可以改善API文档中API警告的可访问性。

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