摘要:自然语言文本形式的文档是软件项目的重要组成部分.如何帮助开发者在大量文档中进行高效、准确的信息定位，是软件复用领域中的一个重要研究问题.提出了一种基于代码结构知识的软件文档语义搜索方法.该方法从软件项目的源代码中解析出代码结构图，并以此作为领域特定的知识来帮助机器理解自然语言文本的语义.这一语义信息与信息检索技术相结合，从而实现了对软件文档的语义检索.在StackOverflow问答文档数据集上的实验表明，与多种文本检索方法相比，该方法在平均准确率（mean average precision，简称MAP）上可以取得至少13.77%的提升.



Abstract:Natural language text is a common form of knowledge representation in various software artifacts. During the practice of software reuse, software developers usually need to search the large amount of textual resource. This paper presents a software text semantic search approach based on code structure knowledge. This approach extracts a code structure graph from software source code and leverages it as a domain-specific knowledge base to analyze the semantic meanings of natural language texts. The semantic information is combined with information retrieval technology to re-rank text search results semantically. Experimental results on StackOverflow dataset show that this approach achieves at least 13.77% improvement in mean average precision (MAP) comparing to several text retrieval approaches.



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