Application of Computational Intelligence for Source Code Classification

Abstract

Multi-language Source Code Management systems have been largely used to collaboratively 
manage software development projects. These systems represent a fundamental step in order 
to fully use communication enhancements by producing concrete value on the way people 
collaborate to produce more reliable computational systems. These systems evaluate results 
of analyses in order to organise and optimise source code. These analyses are strongly dependent 
on technologies (i.e. framework, programming language, libraries) each of them with their own 
characteristics and syntactic structure. To overcome such limitation, source code classification is 
an essential preprocessing step to identify which analyses should be evaluated. This paper introduces 
a new approach for generating content-based classifiers by using Evolutionary Algorithms. Experiments 
were performed on real world source code collected from more than 200 different open source projects. 
Results show us that our approach can be successfully used for creating more accurate source code 
classifiers. The resulting classifier is also expansible and flexible to new classification scenarios 
(opening perspectives for new technologies).