典型文献
A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning
文献摘要：
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.
文献关键词：
中图分类号：
作者姓名：
Shiqi Tang;Song Huang;Changyou Zheng;Erhu Liu;Cheng Zong;Yixian Ding
作者机构：
Command & Control Engineering College,Army Engineering University of PLA,Nanjing 210000,China;Foreign Language College,Liaoning Technical University,Fuxin 123000,China
文献出处：
引用格式：
[1]Shiqi Tang;Song Huang;Changyou Zheng;Erhu Liu;Cheng Zong;Yixian Ding-.A Novel Cross-Project Software Defect Prediction Algorithm Based on Transfer Learning)[J].清华大学学报自然科学版（英文版）,2022(01):41-57
A类：
TSboostDF
B类：
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AB值：
0.630897
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机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
