Volume 31 Issue 2
Mar.  2022
Turn off MathJax
Article Contents
LEI Tianwei, XUE Jingfeng, WANG Yong, NIU Zequn, SHI Zhiwei, ZHANG Yu. WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning[J]. Chinese Journal of Electronics, 2022, 31(2): 354-366. doi: 10.1049/cje.2021.00.119
Citation: LEI Tianwei, XUE Jingfeng, WANG Yong, NIU Zequn, SHI Zhiwei, ZHANG Yu. WCM-WTrA: A Cross-Project Defect Prediction Method Based on Feature Selection and Distance-Weight Transfer Learning[J]. Chinese Journal of Electronics, 2022, 31(2): 354-366. doi: 10.1049/cje.2021.00.119

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

doi: 10.1049/cje.2021.00.119
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) .
More Information
  • 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
  • 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.
  • loading
  • [1]
    Hall T, Beecham S, Bowes D, et al., “A systematic literature review on fault prediction performance in software engineering,” IEEE Transactions on Software Engineering, vol.38, no.6, pp.1276–1304, 2012.
    [2]
    Chen Xiang, Wang Liping, Gu Qing, et al., “A survey on cross-project software defect prediction methods,” Chinese Journal of Computers, vol.41, no.1, pp.255–274, 2018.
    [3]
    Briand L C, Melo W L, and Wust J, “Assessing the applicability of fault-proneness models across object-oriented software projects,” IEEE Transactions on Software Engineering, vol.28, no.7, pp.706–720, 2002. doi: 10.1109/TSE.2002.1019484
    [4]
    Jureczko M and Madeyski L, “Cross-project defect prediction with respect to code ownership model: An empirical study,” E-Informatica Software Engineering Journal, vol.9, no.1, pp.21–35, 2015.
    [5]
    Turhan B, “On the dataset shift problem in software engineering prediction models,” Empirical Software Engineering, vol.17, no.1–2, pp.62–74, 2012.
    [6]
    Nam, Jaechang, S. J. Pan, and S. Kim, “Transfer defect learning,” 2013 35th International Conference on Software Engineering (ICSE), San Francisco, USA, pp. 382–391, 2013.
    [7]
    Pan S J, Tsang I W, Kwok J T, et al., “Domain adaptation via transfer component analysis,” IEEE Transactions on Neural Networks, vol.22, no.2, pp.199–210, 2011. doi: 10.1109/TNN.2010.2091281
    [8]
    Ni Chao, Chen Xiang, Liu Wangshu, et al., “Cross-project defect prediction method based on feature transfer and instance transfer,” Journal of Software, vol.30, no.5, pp.1308–1329, 2019. (in Chinese)
    [9]
    Dai Wenyuan, Yang Qiang, Xue Guirong, et al., “Boosting for transfer learning,” in Proc. of the Twenty-Fourth International Conference on Machine Learning (ICML 2007), Corvallis, Oregon, USA, pp.192–200, 2007.
    [10]
    He Peng, Li Bing, Liu Xiao, et al., “An empirical study on software defect prediction with a simplified metric set,” Information & Software Technology, vol.59, no.1, pp.170–190, 2015.
    [11]
    Turhan B, Menzies T, Bener A B, et al., “On the relative value of cross-company and within-company data for defect prediction,” Empirical Software Engineering, vol.14, no.5, pp.540–578, 2009. doi: 10.1007/s10664-008-9103-7
    [12]
    He Peng, Li B, and Ma Yutao, “Towards cross-project defect prediction with imbalanced feature sets,” arXiv preprint, arXiv: 1411.4228, 2014.
    [13]
    Nam J, Fu W, Kim S, et al., “Heterogeneous Defect Prediction,” IEEE Transactions on Software Engineering, vol.44, no.9, pp.874–896, 2018. doi: 10.1109/TSE.2017.2720603
    [14]
    Ni Chao, Xia Xin, Lo David, et al., “Revisiting supervised and unsupervised methods for effort-aware cross-project defect prediction,” IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2020.3001739, 2020.
    [15]
    Wei Hua, Shan Chun, Hu Changzhen, et al., “Software defect prediction via deep belief network,” Chinese Journal of Electronics, vol.28, no.5, pp.925–932, 2019. doi: 10.1049/cje.2019.06.012
    [16]
    Peters F, Menzies T, and Marcus A, “Better cross company defect prediction,” 2013 10th Working Conference on Mining Software Repositories (MSR), San Francisco, USA, pp.409–418, 2013.
    [17]
    Kamishima T, Hamasaki M, and Akaho S, “TrBagg: A simple transfer learning method and its application to personalization in collaborative tagging,” 2009 Ninth IEEE International Conference on Data Mining, Miami, USA, pp. 219–228, 2009.
    [18]
    Ma Ying, Luo Guangchun, Zeng Xue, et al., “Transfer learning for cross-company software defect prediction,” Information & Software Technology, vol.54, no.3, pp.248–256, 2012.
    [19]
    Chen Lin, Fang Bin, Shang Zhaowei, et al., “Negative samples reduction in cross-company software defects prediction,” Information & Software Technology, vol.62, no.1, pp.67–77, 2015.
    [20]
    Yuan Zhidan, Chen Xiang, Cui Zhanqi, et al., “ALTRA: Cross-project software defect prediction via active learning and TrAdaBoost,” IEEE Access, vol.8, no.1, pp.30037–30049, 2020.
    [21]
    Gong Lina, Jiang Shujuan, Bo Lili, et al., “A novel class-imbalance learning approach for both within-project and cross-project defect prediction,” IEEE Transactions on Reliability, vol.69, no.1, pp.40–54, 2020. doi: 10.1109/TR.2019.2895462
    [22]
    Chen Yong, Xu Chao, He Yanxiang, et al., “The research of compilation optimization on software defect prediction,” Acta Electronica Sinica, vol.49, no.2, pp.216–224, 2021. (in Chinese)
    [23]
    Frogner C, Zhang C, Mobahi H, et al. “Learning with a wasserstein loss,” Proc. of the 28th International Conference on Neural Information Processing Systems (NIPS 2015), Montreal, Canada, pp.2053–2061, 2015.
    [24]
    Reshef D N, Reshef Y A, Finucane H K, et al., “Detecting novel associations in large data sets,” Science, vol.334, no.6062, pp.1518–1524, 2011. doi: 10.1126/science.1205438
    [25]
    Kira K and Rendell L A, “A practical approach to feature selection,” Proc. of the Ninth International Workshop on Machine Learning (ML 1992), Aberdeen, United Kingdom, pp.249–256, 1992.
    [26]
    Yong Guo, “Research of transfer learning based on single-source and multi-source,” Ph.D. Thesis, Xidian University, China, 2013. (in Chinese)
    [27]
    Yao Yi and Doretto G, “Boosting for transfer learning with multiple sources,” The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, USA, pp.1855–1862, 2010.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(7)  / Tables(1)

    Article Metrics

    Article views (445) PDF downloads(52) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return