Combining Particle Swarm Optimization Based Feature Selection and Bagging Technique for Software Defect Prediction

Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Enterprise Software
Format: PDF
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.

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