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

LEI Tianwei; XUE Jingfeng; WANG Yong; NIU Zequn; SHI Zhiwei; ZHANG Yu

doi:10.1049/cje.2021.00.119

Volume 31 Issue 2

Mar. 2022

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Article Navigation > Chinese Journal of Electronics > 2022 > 31(2): 354-366

LEI Tianwei, XUE Jingfeng, WANG Yong, et al., “WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning,” Chinese Journal of Electronics, vol. 31, no. 2, pp. 354-366, 2022, doi: 10.1049/cje.2021.00.119

Citation:

LEI Tianwei, XUE Jingfeng, WANG Yong, et al., “WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning,” Chinese Journal of Electronics, vol. 31, no. 2, pp. 354-366, 2022, doi: 10.1049/cje.2021.00.119

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

doi: 10.1049/cje.2021.00.119

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

Funds: This work was 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), and Fundamental Research Funds for Beijing Universities of Civil Engineering and Architecture (X20069) .

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Author Bio:
was born in 1993. She received the B.E. degree in software engineering from Beijing Institute of Technology. She is a Ph.D. candidate of Beijing Institute of Technology. Her research interests include software fault and malware analysis. (Email: absherry123@163.com)

was born in 1975. He is a Professor and Ph.D. supervisor in Beijing Institute of Technology. His main research interests focus on network security, data security and software security

(corresponding author) was born in 1975. She is an Associate Professor of Beijing Institute of Technology. Her main research interests focus on cyber security and machine learning.(Email: wangyong@bit.edu.cn)

was born in 1994. He is a Ph.D. candidate of Beijing Institute of Technology. His research interests include data mining and malware analysis
Received Date: 2021-03-07
Accepted Date: 2021-07-16

Available Online: 2021-08-27

Publish Date: 2022-03-05

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.
- Cross-project defect prediction,
- Feature engineering,
- Feature selection,
- Distance weight

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References(27)

References

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