RFC: a feature selection algorithm for software defect prediction

• 来源期刊: 系统工程与电子技术（英文版）
• 年，卷（期）: 2021,（2）
• 所属期刊栏目: ELECTRONICS TECHNOLOGY
• 页码范围: 389-398
• 页数: 10页
• 语种: 英文
• DOI: 10.23919/JSEE.2021.000032

摘要

Software defect prediction (SDP) is used to perform the statistical analysis of historical defect data to find out the distribution rule of historical defects, so as to effectively predict defects in the new software. However, there are redundant and irrelevant features in the software defect datasets affecting the performance of defect predictors. In order to identify and re-move the redundant and irrelevant features in software defect datasets, we propose ReliefF-based clustering (RFC), a cluster-based feature selection algorithm. Then, the correlation between features is calculated based on the symmetric uncertainty. Ac-cording to the correlation degree, RFC partitions features into k clusters based on the k-medoids algorithm, and finally selects the representative features from each cluster to form the final feature subset. In the experiments, we compare the proposed RFC with classical feature selection algorithms on nine National Aeronautics and Space Administration (NASA) software defect prediction datasets in terms of area under curve (AUC) and F-value. The experimental results show that RFC can effectively improve the performance of SDP.