WCM-WTrA:A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning  

作　　者：LEI Tianwei XUE Jingfeng WANG Yong NIU Zequn SHI Zhiwei ZHANG Yu 

机构地区：[1]School of Computer Science and Technology,Beijing Institute of Technology,Beijing 100081,China [2]China Information Technology Security Evaluation Center,Beijing 100085,China [3]School of Electrical and Information Engineering,Beijing University of Civil Engineering and Architecture,Beijing 100044,China

出　　处：《Chinese Journal of Electronics》2022年第2期354-366,共13页电子学报（英文版）

基　　金：supported by the National Key Research&Development Program of China(2020YFB1712104);Major Scientific and Technological Innovation Projects of Shandong Province(2020CXGC010116);the National Natural Science Foundation of China(61876019,U1936218,62072037);Zhejiang Lab(2020LE0AB02);Fundamental Research Funds for Beijing Universities of Civil Engineering and Architecture(X20069)。

摘　　要：Cross-project defect prediction is a hot topic in the field of defect prediction.How to reduce the difference between projects and make the model have better accuracy is the core problem.This paper starts from two perspectives:feature selection and distance-weight instance transfer.We reduce the differences between projects from the perspective of feature engineering and introduce the transfer learning technology to construct a cross-project defect prediction model WCM-WTrA and multi-source model Multi-WCM-WTrA.We have tested on AEEEM and ReLink datasets,and the results show that our method has an average improvement of 23%compared with TCA+algorithm on AEEEM datasets,and an average improvement of 5%on ReLink datasets.

关 键 词：Cross-project defect prediction Fea-ture engineering Feature selection Distance weight 

分 类 号：TP311.5[自动化与计算机技术—计算机软件与理论]