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Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction

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Abstract

Cross-project defect prediction (CPDP) refers to predicting defects in a target project using prediction models trained from historical data of other source projects. And CPDP in the scenario where source and target projects have different metric sets is called heterogeneous defect prediction (HDP). Recently, HDP has received much research interest. Existing HDP methods only consider the linear correlation relationship among the features (metrics) of the source and target projects, and such models are insufficient to evaluate nonlinear correlation relationship among the features. So these methods may suffer from the linearly inseparable problem in the linear feature space. Furthermore, existing HDP methods do not take the class imbalance problem into consideration. Unfortunately, the imbalanced nature of software defect datasets increases the learning difficulty for the predictors. In this paper, we propose a new cost-sensitive transfer kernel canonical correlation analysis (CTKCCA) approach for HDP. CTKCCA can not only make the data distributions of source and target projects much more similar in the nonlinear feature space, where the learned features have favorable separability, but also utilize the different misclassification costs for defective and defect-free classes to alleviate the class imbalance problem. We perform the Friedman test with Nemenyi’s post-hoc statistical test and the Cliff’s delta effect size test for the evaluation. Extensive experiments on 28 public projects from five data sources indicate that: (1) CTKCCA significantly performs better than the related CPDP methods; (2) CTKCCA performs better than the related state-of-the-art HDP methods.

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Notes

  1. The left side of “\( \Rightarrow \)” denotes the source project and the right side of “\( \Rightarrow \)” denotes the target project

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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 supported by the National Key Research and Development Program of China under Grant No. 2017YFB0202001, the National Nature Science Foundation of China under Grant Nos. 61272273, 61373038, 61672392, 61472178, 61672208, U1404618, the National Basic Research 973 Program of China under Project No. 2014CB340702, the Program of State Key Laboratory of Software Engineering under Grant No. SKLSE-1216-14, the Natural Science Foundation of Jiangsu Province under Grant No. BK20170900, the Scientific Research Staring Foundation for Introduced Talents in NJUPT under NUPTSF No. NY217009, the Science and Technology Program in Henan province under Grant No. 1721102410064, the Science and Technique Development Program of Henan under Grant No. 172102210186, and the Province-School-Region Project of Henan University under Grant No. 2016S11.

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Authors and Affiliations

  1. State Key Laboratory of Software Engineering, School of Computer, Wuhan University, Wuhan, 430072, China

    Zhiqiang Li, Xiao-Yuan Jing, Xiaoke Zhu, Baowen Xu & Shi Ying

  2. School of Automation, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Xiao-Yuan Jing & Fei Wu

  3. School of Computer and Information Engineering, Henan University, Kaifeng, 475001, China

    Xiaoke Zhu

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  1. Zhiqiang Li
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Correspondence to Xiao-Yuan Jing.

Appendix

Appendix

To replicate the experiments, we publish the source code and datasets of CTKCCA, which can be downloaded from: https://sites.google.com/site/cstkcca/.

Tables 18, 19, 20, 21 and 22 show the detail Cliff’s delta effect size test on Pd, Pf, FM, GM and AUC for each target, respectively.

Table 18 Cliff’s effect size test on Pd for each target
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Table 19 Cliff’s effect size test on Pf for each target
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Table 20 Cliff’s effect size test on FM for each target
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Table 21 Cliff’s effect size test on GM for each target
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Table 22 Cliff’s effect size test on AUC for each target
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Li, Z., Jing, XY., Wu, F. et al. Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction. Autom Softw Eng 25, 201–245 (2018). https://doi.org/10.1007/s10515-017-0220-7

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