Software defect prediction using relational association rule mining

This paper focuses on the problem of defect prediction, a problem of major importance during software maintenance and evolution. It is essential for software developers to identify defective software modules in order to continuously improve the quality of a software system. As the conditions for a software module to have defects are hard to identify, machine learning based classification models are still developed to approach the problem of defect prediction. We propose a novel classification model based on relational association rules mining. Relational association rules are an extension of ordinal association rules, which are a particular type of association rules that describe numerical orderings between attributes that commonly occur over a dataset. Our classifier is based on the discovery of relational association rules for predicting whether a software module is or it is not defective. An experimental evaluation of the proposed model on the open source NASA datasets, as well as a comparison to similar existing approaches is provided. The obtained results show that our classifier overperforms, for most of the considered evaluation measures, the existing machine learning based techniques for defect prediction. This confirms the potential of our proposal. (C) 2014 Elsevier Inc. All rights reserved.