关键词: 跨项目软件缺陷预测, 无关信息, 数据冗余, 代价敏感, 同构度量

Abstract: For the process of cross-project software defect prediction, software defect data has irrelevant information or data redundancy, cross-project software defect prediction based on Multi-Policy Feature Filtering（MPFF） method is proposed. Firstly, multi-strategy screening method and oversampling method are used for data preprocessing. Then cost-sensitive domain adaptive method is used for classification. The classification process uses a small amount of labeled target project data to improve the distribution difference among projects. Finally, different metric prediction experiments are performed on the AEEEM, NASA MDP, and SOFTLAB data sets. Different metric prediction experiments are performed on the data set. The experimental results show that the MPSDA method has the best performance compared with the Burank filter, Peters filter, TCA+ and TrAdaBoost methods under the homogeneous metric.

Key words: cross-project software defect prediction, irrelevant information；data redundancy, cost sensitive, homogeneous metric