Abstract: In the task of software defect prediction,the machine learning classification algorithm is usually used to build a software defect prediction(SDP) model based on dataset with static softwarefeatures such as C&K metrics.However,the number of defects in most datasets with static software metrics is small,the class imbalance in the dataset is serious,resulting in the low prediction performance of the model.Based on generation adversarial network(GAN),this paper uses FID score screening to ge-nerate positive sample data,enhances the amount of postitive data,and then aggregates the results of learned models by majority-voting,and finally build the SDP model based on block-regularized m×2 Cross validation(m×2BCV).20 datasets in PROMISE database are used as the experimental datasets,and random forest algorithm is used to build model.Experimental results show that,compared with the traditional random over-sampling,SMOTE,and random under-sampling,the average F1 values of the SDP aggregation model in the 20 datasets is increased by 10.2%,5.7%,and 3.4% respectively,and the stability of F1 is also improved accordingly.In 17 of the 20 datasets,the SDP aggregation models have the highest F1 values.From the AUC index,there is no significant difference between the proposed method and the traditional sampling method.

Key words: Generative adversarial network, Data enhancement, Block-regularized m×2 cross validation, Software defect prediction, Aggregation model