Mining Analogical Libraries in Q&A Discussions -- Incorporating Relational and Categorical Knowledge into Word Embedding

Abstract

Third-party libraries are an integral part of many software projects. It often happens that developers need to find analogical libraries that can provide comparable features to the libraries they are already familiar with. Existing methods to find analogical libraries are limited by the community-curated list of libraries, blogs, or Q&A posts, which often contain overwhelming or out-of-date information. In this paper, we present a new approach to recommend analogical libraries based on a knowledge base of analogical libraries mined from tags of millions of Stack Overflow questions. The novelty of our approach is to solve analogical-libraries questions by combining state-of-the-art word embedding technique and domain-specific relational and categorical knowledge mined from Stack Overflow. We implement our approach in a proof-of-concept web application (https://graphofknowledge.appspot.com/similartech). The evaluation results show that our approach can make accurate recommendation of analogical libraries (Precision@1=0.81 and Precision@5=0.67). Google Analytics of the website traffic provides initial evidence of the potential usefulness of our web application for software developers.