典型文献
A software defect prediction method with metric compensation based on feature selection and transfer learning
文献摘要：
Cross-project software defect prediction solves the problem of insufficient training data for traditional defect prediction, and overcomes the challenge of applying models learned from multiple different source projects to target project. At the same time, two new problems emerge: (1) too many irrelevant and redundant features in the model training process will affect the training efficiency and thus decrease the prediction accuracy of the model;(2) the distribution of metric values will vary greatly from project to project due to the development environment and other factors, resulting in lower prediction accuracy when the model achieves cross-project prediction. In the proposed method, the Pearson feature selection method is introduced to address data redundancy, and the metric compensation based transfer learning technique is used to address the problem of large differences in data distribution between the source project and target project. In this paper, we propose a software defect prediction method with metric compensation based on feature selection and transfer learning. The experimental results show that the model constructed with this method achieves better results on area under the receiver operating characteristic curve (AUC) value and F1-measure metric.
文献关键词：
中图分类号：
作者姓名：
Jinfu CHEN;Xiaoli WANG;Saihua CAI;Jiaping XU;Jingyi CHEN;Haibo CHEN
作者机构：
School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,China;Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace,Jiangsu University,Zhenjiang 212013,China
文献出处：
引用格式：
[1]Jinfu CHEN;Xiaoli WANG;Saihua CAI;Jiaping XU;Jingyi CHEN;Haibo CHEN-.A software defect prediction method with metric compensation based on feature selection and transfer learning)[J].信息与电子工程前沿（英文）,2022(05):715-731
A类：
B类：
software,defect,prediction,method,metric,compensation,selection,transfer,learning,Cross,solves,insufficient,training,data,traditional,overcomes,challenge,applying,models,learned,from,multiple,different,source,projects,target,At,same,two,new,problems,emerge,too,many,irrelevant,redundant,features,process,will,affect,efficiency,thus,decrease,accuracy,distribution,values,vary,greatly,due,development,environment,other,factors,resulting,lower,when,achieves,cross,In,proposed,introduced,address,redundancy,technique,used,large,differences,between,this,paper,experimental,results,show,that,constructed,better,area,under,receiver,operating,characteristic,curve,measure
AB值：
0.482114
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