Towards an Automated Classification of Software Libraries

Abstract

Nowadays, the use of third-party libraries in software is common. At the same time, the number of published libraries continues to increase. An automated classification should help to maintain an overview and identify similar software libraries. This paper investigates if new approaches can be used to classify all software libraries crawled from Apache Maven repositories into defined classes using machine learning. In addition to tags that are not always available or of poor quality, we examine one feature that is always available—the id. Consisting of group-id and artifact-id, the id of an Apache Maven software library contains valuable information that can help in classification. Through a developed preprocessing and an optimized recurrent neural network (RNN), the tokenised ids should allow a classification of most libraries. Furthermore, we present an optimized approach through a hybrid use of id tokens and tags in combination. Based on the dataset including 28,600 labeled entries, a comparison of various approaches was carried out. The RNN achieved a balanced accuracy of 71.36% by training on tokenised ids. A model trained on tags achieved a balanced accuracy of 92%. However, the new hybrid approach, which combines tags and ids, optimizes the result to 94.12%. While a classification on tags achieves a better result than the more general id-based approach, the applicability is limited to software libraries that are tagged. The hybrid approach, on the other hand, takes advantage of the classification results based on tags when these are available, but includes valuable information from the always available ids.