Adaptive Approach of Fault Prediction in Software Modules by Using Discriminative and Generative Model of Machine Learning
Software quality assurance is the most important activity during the development of software. Defective software modules may increase costs and decrease customer satisfaction. Hence, effective defect prediction models or techniques are very important in order to deliver efficient software. In this paper, different machine learning algorithms are used to predict three main prediction performance measures i.e. precision, recall and f-measure. The accuracy of the software modules is being calculated. Different classifiers are also used in order to predict the values of these measures by using important attributes only.