关键词: 软件缺陷数量预测, 密度峰值聚类, 回环特征选择, 反距离加权法, 集成学习

Abstract:

Software defect prediction is a hot research topic in software engineering. Generally, the research work of software defect prediction mainly focuses on whether there are defects in software modules and the number of defects in software modules. At present, the research of software defect quantity mainly focuses on software module sequencing based on defect quantity. In order to improve the accuracy of software module sorting, this paper proposes a prediction model of loopback software defects. This model includes two parts：loop feature selection and defect prediction. In the loop feature selection part, the improved density peak clustering algorithm and the wrapped feature selection method are combined to dynamically select the optimal feature in the loop way and train the learner. In the defect prediction part, the inverse distance weighted integration method is used to get the prediction results. The experimental results show that compared with LRCR, GRCR, LR, MLP, GP, NBR and ZIP, the model is improved by 10.36%, 28.74%, 13.51%, 36.61%, 25.30%, 60.14% and 54.72% respectively, it is helpful to improve the efficiency of software testing.

Key words: software defect quantity measurement, density peak clustering, loop feature selection, inverse distance weighting method, integrated learning