Application of support vector machine to predict fault prone classes

Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. It would also be interesting to know the relationship between object-oriented metrics and fault proneness. In this paper, we build a Support Vector Machine (SVM) model to find the relation-ship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using open source software. The performance of the SVM method was evaluated by Receiver Operating Characteristic (ROC) analysis. Based on these results, it is reasonable to claim that such models could help for planning and performing testing by focusing resources on fault- prone parts of the design and code. Thus, the study shows that SVM method may also be used in constructing software quality models. However, similar types of studies are required to be carried out in order to establish the acceptability of the model.