Topic Recommendation for Software Repositories using Multi-label Classification Algorithms

TL;DR

This paper applies multi-label classification algorithms to predict and recommend relevant GitHub repository topics using textual data, achieving high accuracy and creating an online tool for automatic annotation.

Contribution

The study introduces a multi-label classification approach for automatic repository topic prediction, leveraging textual information and mapping user-defined topics to curated GitHub featured topics.

Findings

Achieved Recall@5 of 0.890 and LRAP of 0.805 in topic prediction

Model effectively recommends complete and accurate topic sets based on user assessments

Developed and released an online tool for automatic repository topic annotation

Abstract

Many platforms exploit collaborative tagging to provide their users with faster and more accurate results while searching or navigating. Tags can communicate different concepts such as the main features, technologies, functionality, and the goal of a software repository. Recently, GitHub has enabled users to annotate repositories with topic tags. It has also provided a set of featured topics, and their possible aliases carefully curated with the help of the community. This creates the opportunity to use this initial seed of topics to automatically annotate all remaining repositories, by training models that recommend high-quality topic tags to developers. In this work, we study the application of multi-label classification techniques to predict software repositories' topics. First, we map the large space of user-defined topics to those featured by GitHub. The core idea is to derive more information from projects' available documentation. Our data contains about $152$K GitHub repositories and $228$ featured topics. Then, we apply supervised models on repositories' textual information such as descriptions, README files, wiki pages, and file names. We assess the performance of our approach both quantitatively and qualitatively. Our proposed model achieves Recall@5 and LRAP scores of $0.890$ and $0.805$, respectively. Moreover, based on users' assessment, our approach is highly capable of recommending a correct and complete set of topics. Finally, we use our models to develop an online tool named \texttt{Repository Catalogue}, that automatically predicts topics for GitHub repositories and is publicly available.