Science & Engineering Research Support soCiety (SERSC)
The costs of finding and correcting software defects have been the most expensive activity in software development. The accurate prediction of defect-prone software modules can help the software testing effort, reduce costs and improve the software testing process by focusing on fault-prone module. Recently, static code attributes are used as defect predictors in software defect prediction research, since they are useful, generalizable, easy-to-use and widely used. However, two common aspects of data quality that can affect performance of software defect prediction are class imbalance and noisy attributes.