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.
The first author is a student.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Boehm, B.W.: Industrial software metrics top 10 list. IEEE Softw. 4(5), 84–85 (1987)
Camargo Cruz, A.E., Ochimizu, K.: Towards logistic regression models for predicting fault-prone code across software projects. In: International Symposium on Empirical Software Engineering and Measurement, pp. 460–463 (2009)
Liu, C., Yang, D., Xia, X., Yan, M., Zhang, X.: A two-phase transfer learning model for cross-project defect prediction. Inf. Softw. Technol. 107, 125–136 (2019)
Wu, F., et al.: Intraspectrum discrimination and interspectrum correlation analysis deep network for multispectral face recognition. IEEE Trans. Cybern. 1–14 (2018)
Wu, F., et al.: Cross-project and within-project semisupervised software defect prediction: a unified approach. IEEE Trans. Reliab. 67(2), 581–597 (2018)
Tong, H., Liu, B., Wang, S.: Software defect prediction using stacked denoising autoencoders and two-stage ensemble learning. Inf. Softw. Technol. 96, 94–111 (2018)
Herbold, S.: Training data selection for cross-project defect prediction. In: International Conference on Predictive Models in Software Engineering, p. 6 (2013)
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Process. Mag. 29(6), 82–97 (2012)
Deng, J., Xia, R., Zhang, Z., Liu, Y., Schuller, B.: Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition. In: International Conference on Acoustics, Speech and Signal Processing, pp. 4818–4822 (2014)
Deng, J., Zhang, Z., Eyben, F., Schuller, B.: Autoencoder-based unsupervised domain adaptation for speech emotion recognition. IEEE Signal Process. Lett. 21(9), 1068–1072 (2014)
Jureczko, M., Madeyski, L.: Towards identifying software project clusters with regard to defect prediction. In: International Conference on Predictive Models in Software Engineering, p. 9 (2010)
Minku, L., Sarro, F., Mende, E., Ferrucci, F.: How to make best use of cross-company data for web effort estimation? In: International Symposium on Empirical Software Engineering and Measurement, pp. 1–10 (2015)
Zhao, L., Shang, Z., Zhao, L., Qin, A., Tang, Y.Y.: Siamese dense neural network for software defect prediction with small data. IEEE Access 7, 7663–7677 (2019)
Nam, J., Pan, S.J., Kim, S.: Transfer defect learning. In: International Conference on Software Engineering, pp. 382–391 (2013)
Wang, S., Yao, X.: Using class imbalance learning for software defect prediction. IEEE Trans. Reliab. 62(2), 434–443 (2013)
Wang, S., Liu, T., Tan, L.: Automatically learning semantic features for defect prediction. In: International Conference on Software Engineering, pp. 297–308 (2016)
Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E.: Data preprocessing for supervised learning. Int. J. Comput. Sci. 1(2), 111–117 (2006)
Kim, S., Zhang, H., Wu, R., Gong, L.: Dealing with noise in defect prediction. In: International Conference on Software Engineering, pp. 481–490 (2011)
Liu, W., Liu, S., Gu, Q., Chen, J., Chen, X., Chen, D.: Empirical studies of a two-stage data preprocessing approach for software fault prediction. IEEE Trans. Reliab. 65(1), 38–53 (2016)
Yang, X., Lo, D., Xia, X., Zhang, Y., Sun, J.: Deep learning for just-in-time defect prediction. In: International Conference on Software Quality, Reliability and Security, pp. 17–26 (2015)
Gao, Y., Yang, C., Liang, L.: Software defect prediction based on geometric mean for subspace learning. In: Advanced Information Technology, Electronic and Automation Control Conference, pp. 225–229 (2017)
Yang, Y., et al.: Are slice-based cohesion metrics actually useful in effort-aware post-release fault-proneness prediction? An empirical study. IEEE Trans. Softw. Eng. 41(4), 331–357 (2015)
Li, Z., Jing, X., Wu, F., Zhu, X., Xu, B., Ying, S.: Cost-sensitive transfer kernel canonical correlation analysis for heterogeneous defect prediction. Autom. Softw. Eng. 25(2), 201–245 (2018)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-31726-3_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31725-6
Online ISBN: 978-3-030-31726-3
eBook Packages: Computer ScienceComputer Science (R0)