Abstract
Cross-project defect prediction means training a classifier model using the historical data of the other source project, and then testing whether the target project instance is defective or not. Since source and target projects have different data distributions, and data distribution difference will degrade the performance of classifier. Furthermore, the class imbalance of datasets increases the difficulty of classification. Therefore, a cost-sensitive shared hidden layer autoencoder (CSSHLA) method is proposed. CSSHLA learns a common feature representation between source and target projects by shared hidden layer autoencoder, and makes the different data distributions more similar. To solve the class imbalance problem, CSSHLA introduces a cost-sensitive factor to assign different importance weights to different instances. Experiments on 10 projects of PROMISE dataset show that CSSHLA improves the performance of cross-project defect prediction compared with baselines.
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Acknowledgements
The work described in this paper was supported by National Natural Science Foundation of China (No. 61702280), Natural Science Foundation of Jiangsu Province (No. BK20170900), National Postdoctoral Program for Innovative Talents (No. BX20180146), Scientific Research Starting Foundation for Introduced Talents in NJUPT (NUPTSF, No. NY217009), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province KYCX17_0794.
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Li, J., Jing, XY., Wu, F., Sun, Y., Yang, Y. (2019). A Cost-Sensitive Shared Hidden Layer Autoencoder for Cross-Project Defect Prediction. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_42
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