Impact Of Code Smells On Software Fault Prediction At Class Level And Method Level

The main aim of software fault prediction is the identification of such classes and methods where faults are expecting at an early stage using some properties of the project. Early-stage prediction of software faults supports software quality assurance activities. Evaluation of code smells for anticipating software faults is basic to ensure its importance in the field of software quality. In this paper, we investigate the impact of code smells on software fault prediction at the class level and method level. Previous studies show the impact of code smells on fault prediction. However, using code smells for class level faults prediction and method level fault prediction need more concern. We use defects4j repository for the creation of datasets used in building software fault prediction model-based. We use pseudo labeling for class level prediction and bagging for method level prediction. We extract code smells from different classes and methods and then used these extracted code smells for fault prediction. We compare our prediction results with actual results and see if our prediction is correct in order to do validation.