信息工程大学学报2023，Vol.24Issue(6)：691-698,8.DOI:10.3969/j.issn.1671-0673.2023.06.009
面向融合度量的PSO-RBF软件缺陷数量预测模型
PSO-RBF Software Defect Quantity Prediction Model for Fusion Metrics
摘要
Abstract
The existing software defect prediction technology mainly predicts the tendency or number of software defects based on software product metrics or process metrics,and there is a lack of defect prediction research on integrating product metrics and process metrics.To improve the applicability and accuracy of the software defect prediction model for fusion metrics,a software defect quantity prediction model is proposed based on the fusion of software product metrics and process metrics.This model mainly includes two stages:feature selection and defect quantity prediction.At the fea-ture selection stage,a feature selection method combining improved density peak clustering algo-rithm and Pearson correlation coefficient is proposed to complete the feature selection;at the defect quantity prediction stage,based on radial basis function(RBF)neural network,particle swarm opti-mization(PSO)algorithm is introduced to build PSO-RBF software defect quantity prediction model.The experimental results show that the PSO-RBF model for fusion metrics is more effective in defect quantity prediction.关键词
软件缺陷数量预测/融合度量指标/密度峰值聚类算法/粒子群优化算法/径向基函数神经网络Key words
prediction of the number of software defects/fusion metrics/density peak clustering al-gorithm/particle swarm optimization algorithm/radial basis function neural network分类
信息技术与安全科学引用本文复制引用
刘路瑶,韩培胜,李伟群,李万鹏..面向融合度量的PSO-RBF软件缺陷数量预测模型[J].信息工程大学学报,2023,24(6):691-698,8.基金项目
国家自然科学基金资助项目(61572517) （61572517）
