WCM‐WTrA: A Cross‐Project Defect Prediction Method Based on Feature Selection and Distance‐Weight Transfer Learning. Issue 2 (1st March 2022)

• Record Type: Journal Article
• Authors: LEI, Tianwei; XUE, Jingfeng; WANG, Yong; NIU, Zequn; SHI, Zhiwei; ZHANG, Yu
• Is Part Of: Chinese journal of electronics. Volume 31:Issue 2(2022)
• Page Start: 354
• Page End: 366
• Publication Date: 2022-03-01
• Subjects: Cross‐project defect prediction -- Feature engineering -- Feature selection -- Distance weight; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2021.00.119
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.