LAUSR

Abstract In software fault prediction, before beginning with the real software testing process, faultprone software modules are identified with the help of various properties related to the software project. This leads to the achievement of minimal cost apart from the desired software quality. In this study, Object oriented metrics were used to find the main factors. The technique used to find the important predictors is Factor analysis (FA) and regression is used to analyze the goodness of fit of different models drawn from previous studies. Following this, identification of various factors associated with the software fault prediction process is done. In addition, a robust model is developed on the basis of the distinct identified factors. The research work is the extension of our previous work. This research provides a novel approach of different aspects of the software fault prediction process and also assists in discovering the different types of problems associated with the term i.e. software fault prediction. Towards the end, the statistical comparison and the challenges of the paper are discussed along with the future guidelines of the study.