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
• 来源期刊: 清华大学学报自然科学版（英文版）
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: SPECIAL SECTION ON RELIABILITY AND SECURITY
• 页码范围: 41-57
• 页数: 17页
• 语种: 英文
• DOI: 10.26599/TST.2020.9010040

摘要

Software Defect Prediction (SDP) technology is an effective tool for improving software system quality that has attracted much attention in recent years.However,the prediction of cross-project data remains a challenge for the traditional SDP method due to the different distributions of the training and testing datasets.Another 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 CPDP.The experimental results demonstrate that the performance achieved by TSboostDF is better than those of existing CPDP methods.