Applying Feature Selection to Software Defect Prediction Using Multi-objective Optimization

Software defect prediction can identify potential defective modules in advance and then provide guidances for software testers to allocate more testing resources on these modules. During the gathering process for defect prediction datasets, if multiple metrics are used to measure the program modules, it will result in curse of dimensionality. Feature selection is one of effective methods to alleviate this problem. However, designing effective feature selection methods is a great challenge. Motivated by the idea of search based software engineering, we formalize this problem as a multi-objective optimization problem, and then propose novel method MOFES. To verify the effectiveness of our proposed method, we choose PROMISE dataset gathered from real projects, and compare MOFES with some classical baseline methods. Final results show that our method has the advantages of selecting less features and achieving better prediction performance in most projects while its computational cost is acceptable.