Software Defect Prediction Using a Hybrid Model Based on Semantic Features Learned from the Source Code

Software defect prediction has extensive applicability thus being a very active research area in Search-Based Software Engineering. A high proportion of the software defects are caused by violated couplings. In this paper, we investigate the relevance of semantic coupling in assessing the software proneness to defects. We propose a hybrid classification model combining Gradual Relational Association Rules with Artificial Neural Networks, which detects the defective software entities based on semantic features automatically learned from the source code. The experiments we have performed led to results that confirm the interplay between conceptual coupling and software defects proneness.