Software categorization using low-level distributional features
Zalán Bodó, Bipin Indurkhya
In recent years, there has been a growing interest in applying deep learning techniques for automatic generation of software. To achieve this ambitious objective, a number of smaller research goals need to be reached, one of which is automatic categorization of software, used in numerous tasks of software intelligence. We present here an approach to this problem using a set of low-level features derived from lexical analysis of software code. We compare different feature sets for categorizing software and also apply different supervised machine learning algorithms to perform the classification task. The representation allows us to identify the most relevant libraries used for each class, and we use the best-performing classifier to accomplish this. We evaluate our approach by applying it to categorize popular Python projects from Github.