计算机技术与发展2016，Vol.26Issue(11)：54-57,62,5.DOI:10.3969/j.issn.1673-629X.2016.11.012
基于局部稀疏重构度量学习的软件缺陷预测
Software Defect Prediction of Metric Learning Based on Local Sparse Reconstruction
摘要
Abstract
With the development of computer technology,how to predict the potential defects in software project preciously is an important topic. Recently,researchers have introduced some machine learning methods into the software defect prediction field. However,they usual-ly utilize the traditional Euclidean metric in classification phase. Distance metric learning can learn an effective distance metric by exploi-ting the feature and label information of training sets,which makes the original samples hold better discriminability in the new feature space. The distance metric learning is introduced into the software defect prediction field,and a novel software defect prediction approach called Local Sparse Reconstruction based Metric Learning ( LSRML) is proposed. It incorporates the local sparse reconstruction informa-tion into the distance metric learning scheme. The learned distance metric not only has favorable discriminability,but also effectively han-dles the noise problem. The experiment results on the NASA projects demonstrate the effectiveness of the proposed approach.关键词
度量学习/软件缺陷预测/稀疏表示/局部信息/鉴别性Key words
distancemetric learning/software defect prediction/sparse representation/local information/discriminability分类
信息技术与安全科学引用本文复制引用
王晴,荆晓远,朱阳平,吴飞,董西伟,程立..基于局部稀疏重构度量学习的软件缺陷预测[J].计算机技术与发展,2016,26(11):54-57,62,5.基金项目
国家自然科学基金资助项目(61272273) （61272273）
