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Data sampling and kernel manifold discriminant alignment for mixed-project heterogeneous defect prediction

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Abstract

Heterogeneous defect prediction (HDP) refers to identifying more likely defect-proneness of software modules in a target project using heterogeneous metric data from other source projects, which solves the heterogeneous metric problem in cross-project defect prediction. Recently, several mixed-project HDP methods have been presented. However, these models neglect to address the linear inseparability and cross-project class imbalance issues simultaneously. These limitations usually lead to the unsatisfactory performance of HDP. In this paper, we propose an improved transfer learning approach for mixed-project HDP to deal with the above limitations, called data sampling and kernel manifold discriminant alignment (DSKMDA). DSKMDA firstly applies data sampling technique to handle the class imbalance issue. Then it uses kernel manifold discriminant alignment technique to handle the linear inseparability issue. Extensive experiments on 13 projects from three public benchmark datasets with four evaluation measures demonstrate that DSKMDA can produce better or comparable results against a range of competing methods.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their constructive comments and suggestions. This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 61902228, 62041603 and 62176069, the Natural Science Basic Research Program of Shaanxi Province under Grant No. 2020JQ-422, the Natural Science Foundation of Guangdong Province under Grant No. 2019A1515011076, the Innovation Group of Guangdong Education Department under Grant Nos. 2020KCXTD014 and 2018KCXTD019, the 2019 Key Discipline project of Guangdong Province, the Natural Science Foundation of Jiangxi Province under Grant No. 20202BABL202036, the Fundamental Research Funds for the Central Universities under Grant Nos. GK202103083 and GK202105006, and the project of State Key Laboratory for Novel Software Technology under Grant No. KFKT2021B29.

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Authors and Affiliations

  1. School of Computer Science, Shaanxi Normal University, Xi’an, 710119, China

    Jingwen Niu & Zhiqiang Li

  2. School of Computer and Information Engineering, Xinxiang University, Xinxiang, 453003, China

    Jingwen Niu

  3. School of Computer Science, Wuhan University, Wuhan, 430072, China

    Haowen Chen & Xiao-Yuan Jing

  4. School of Computer and Big Data Science, Jiujiang University, Jiujiang, 332005, China

    Xiwei Dong

  5. School of Computer Science and Guangdong Provincial Key Laboratory on Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming, 525000, China

    Xiao-Yuan Jing

  6. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, China

    Xiao-Yuan Jing

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  1. Jingwen Niu
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Niu, J., Li, Z., Chen, H. et al. Data sampling and kernel manifold discriminant alignment for mixed-project heterogeneous defect prediction. Software Qual J 30, 917–951 (2022). https://doi.org/10.1007/s11219-022-09588-z

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