An analysis of aggregated coupling's suitability for software defect prediction

Software Defect Prediction is an important problem during the development of a software system, because it helps to focus the testing effort on those parts of the system which have a high probability of being defective. It is also well-researched, there being many papers presenting Machine Learning-based prediction models for this problem. But most of them use the same object-oriented structural software metrics as features. In this paper we investigate the impact of aggregated coupling, which combines structural and conceptual coupling, on software defects proneness. In this regard, we present three software metrics suites derived from both structural and conceptual coupling and analyze how their different combinations influence the performance of software defect prediction models. We analyze the relative performance of the models when using features extracted with LSI versus Doc2Vec in conjunction with Cosine versus Euclidean similarity for computing the conceptual coupling. The results suggest that all these features are complementary and their usage improves the performance of the machine learning models.