FAULT PREDICTION USING STATISTICAL AND MACHINE LEARNING METHODS FOR IMPROVING SOFTWARE QUALITY

Abstract

Empirical validation of metrics to predict the quality attributes is essential in order to gain insight about the quality of software in early phases of software development. The early indication of quality attributes is relevant to the software organization. In any software organization, there is always a demand for reducing the development cost, decreasing the development time, increasing the software reliability and making the software more efficient. In this paper, we predict a model to estimate fault proneness using Object Oriented CK metrics, QMOOD metrics and some more. We apply one statistical method and six machine learning methods to predict the models. The proposed models are validated using dataset collected from Open Source software. The results are analyzed using Area Under the Curve (AUC) obtained from Receiver Operating Characteristics (ROC) analysis. The results show that the machine learning methods outperformed the statistical method. Among the machine learning methods, random forest and bagging showed the best results. Thus, researchers and practitioners may use them in their future studies to predict the faulty classes. Based on these results it is reasonable to claim that quality models have a significant relevance with Object Oriented metrics and machine learning methods have comparable performance with statistical methods.