Improving software text retrieval using conceptual knowledge in source code

摘要

A large software project usually has lots of various textual learning resources about its API, such as tutorials, mailing lists, user forums, etc. Text retrieval technology allows developers to search these API learning resources for related documents using free-text queries, but it suffers from the lexical gap between search queries and documents. In this paper, we propose a novel approach for improving the retrieval of API learning resources through leveraging software-specific conceptual knowledge in software source code. The basic idea behind this approach is that the semantic relatedness between queries and documents could be measured according to software-specific concepts involved in them, and software source code contains a large amount of software-specific conceptual knowledge. In detail, firstly we extract an API graph from software source code and use it as software-specific conceptual knowledge. Then we discover API entities involved in queries and documents, and infer semantic document relatedness through analyzing structural relationships between these API entities. We evaluate our approach in three popular open source software projects. Comparing to the state-of-the-art text retrieval approaches, our approach lead to at least 13.77% improvement with respect to mean average precision (MAP).