Software Fault Proneness Prediction Using Support Vector Machines

Empirical validation of software metrics to predict quality using machine learning methods is important to ensure their practical relevance in the software organizations. In this paper, we build a Support Vector Machine (SVM) model to find the relationship between object-oriented metrics given by Chidamber and Kemerer and fault proneness. The proposed model is empirically evaluated using public domain KC1 NASA data set. 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.