A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning

作     者：Shiqi Tang Song Huang Changyou Zheng Erhu Liu Cheng Zong Yixian Ding Shiqi Tang;Song Huang;Changyou Zheng;Erhu Liu;Cheng Zong;Yixian Ding

作者机构：Command&Control Engineering CollegeArmy Engineering University of PLANanjing 210000China Foreign Language CollegeLiaoning Technical UniversityFuxin 123000China 

出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)

年 卷 期：2022年第27卷第1期

页      面：41-57页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Army Weapons and Equipment Internal Research (No. LJ20191C080690) 

主　　题：Software Defect Prediction(SDP) transfer learning imbalance class cross-project 

摘      要：Software Defect Prediction(SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent ***,the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing *** major difficulty is the class imbalance issue that must be addressed in Cross-Project Defect Prediction(CPDP).In this work,we propose a transfer-leaning algorithm(TSboostDF) that considers both knowledge transfer and class imbalance for *** experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.