GitRanking: A Ranking of GitHub Topics for Software Classification using Active Sampling

TL;DR

GitRanking introduces a hierarchical, knowledge-based ranking system for GitHub topics, utilizing active sampling and Wikidata links to improve software classification and aid developers in better annotating projects.

Contribution

It presents a novel, extensible framework for ranking software topics by generality, integrating active sampling and Wikidata to create a hierarchical taxonomy grounded in a knowledge base.

Findings

Developed a ranked taxonomy of GitHub topics based on their generality.

Demonstrated that developers tend to avoid highly specific terms in annotations.

Showed GitRanking's effectiveness and extensibility with minimal annotations.

Abstract

GitHub is the world's largest host of source code, with more than 150M repositories. However, most of these repositories are not labeled or inadequately so, making it harder for users to find relevant projects. There have been various proposals for software application domain classification over the past years. However, these approaches lack a well-defined taxonomy that is hierarchical, grounded in a knowledge base, and free of irrelevant terms. This work proposes GitRanking, a framework for creating a classification ranked into discrete levels based on how general or specific their meaning is. We collected 121K topics from GitHub and considered $60\%$ of the most frequent ones for the ranking. GitRanking 1) uses active sampling to ensure a minimal number of required annotations; and 2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Our results show that developers, when annotating their projects, avoid using terms with a high degree of specificity. This makes the finding and discovery of their projects more challenging for other users. Furthermore, we show that GitRanking can effectively rank terms according to their general or specific meaning. This ranking would be an essential asset for developers to build upon, allowing them to complement their annotations with more precise topics. Finally, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked with a minimum number of annotations ($\sim$ 15). This paper is the first collective attempt to build a ground-up taxonomy of software domains.