For an unfamiliar Application Programming Interface (API), software developers often access the official documentation to learn its usage, and post questions related to this API on social question and answering (Q&A) sites to seek solutions. The official software documentation often captures the information about functionality and parameters, but lacks detailed descriptions in different usage scenarios. On the contrary, the discussions about APIs on social Q&A sites provide enriching usages. In this paper, we present CnCxL2R, a software documentation recommendation strategy incorporating the content of official documentation and the social context on Q&A into a learning-to-rank schema. In the proposed strategy, the content, local context and global context of documentation are considered to select candidate documents. Then four types of features are extracted to learn a ranking model. We conduct a large-scale automatic evaluation on Java documentation recommendation. The results show that CnCxL2R achieves state-of-the-art performance over the eight baseline models. We also compare the CnCxL2R with Google search. The results show that CnCxL2R can effectively capture the semantic between the high-level intent in developers' queries and the low-level implementation in software documentation.

中文翻译：

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利用官方内容和社会背景来推荐软件文档

对于不熟悉的应用程序接口（API），软件开发者往往会通过查阅官方文档了解其用法，并在社交问答（Q&A）网站上发布与该API相关的问题寻求解决方案。官方的软件文档往往会捕捉到有关功能和参数的信息，但缺乏针对不同使用场景的详细描述。相反，社交问答网站上关于 API 的讨论提供了丰富的用法。在本文中，我们提出了 CnCxL2R，这是一种软件文档推荐策略，将官方文档的内容和问答的社会背景整合到一个学习排名模式中。在提出的策略中，考虑文档的内容、本地上下文和全局上下文来选择候选文档。然后提取四种类型的特征来学习排序模型。我们对Java文档推荐进行了大规模的自动评估。结果表明，CnCxL2R 在八个基线模型上实现了最先进的性能。我们还将 CnCxL2R 与 Google 搜索进行了比较。结果表明，CnCxL2R 可以有效地捕获开发人员查询中的高级意图与软件文档中的低级实现之间的语义。