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Context  GitHub is the world's most prominent host of source code, with more than 327M repositories. However, most of these repositories are not labelled or inadequately, making it harder for users to find relevant projects. Various proposals for software application domain classification over the past years have been proposed. However, these several of those approaches suffer from multiple issues, called  antipatterns  of software classification, that reduce their usability.    Objective  In this paper, we propose a new taxonomy in the GitHub ecosystem, called GitRanking, starting from a well‐structured data set, composed of curated repositories annotated with topics. The main objective is to create a baseline methodology for software classification that is expandable, hierarchical, grounded in a knowledge base, and free of antipatterns.    Method  We collected 121K topics from GitHub and used GitRanking to create a taxonomy of 301 ranked application domains. GitRanking (1) uses  active sampling  to ensure a minimal number of annotations to create the ranking; and (2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Furthermore, we adopt the conceived taxonomy in a classification task by considering a state‐of‐the‐art classifier.    Results  Our results show that GitRanking can effectively rank terms in a hierarchy according to how general or specific their meaning is. Furthermore, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked, and with a minimum number of annotations (    ≈  15   $$ \approx 15 $$   ). Concerning the classification task, we show that the model achieves an F1‐score of 34%, with a precision of 54%.    Conclusion  This paper is the first collective attempt at building a ground‐up taxonomy of software domains. Our vision is that our taxonomy, and its extensibility, can be used to better and more precisely label software projects.

GitRanking: A ranking of GitHub topics for software classification using active sampling