Transfer Learning for Software Defect Prediction

CHENG Ming, WU Guo-qing, YUAN Meng-ting

ACTA ELECTRONICA SINICA ›› 2016, Vol. 44 ›› Issue (1) : 115-122.

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ACTA ELECTRONICA SINICA ›› 2016, Vol. 44 ›› Issue (1) : 115-122. DOI: 10.3969/j.issn.0372-2112.2016.01.017

Transfer Learning for Software Defect Prediction

  • CHENG Ming1,2, WU Guo-qing1,2, YUAN Meng-ting1,2
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Abstract

The traditional software defect prediction methods have weak adaptive ability for cross-project defect prediction, largely because of feature distribution differences between the source and target projects.In order to resolve this problem, we propose a novel weighted naive Bayes transfer learning algorithm.Firstly, the feature information of the test data and training data are collected;next, our solution computes feature differences, and transfers cross-project data differences into the weights of the training data;finally, on these weighted data, the defect prediction model is built.Our experiments are conducted on eight open-source projects, and experimental results demonstrate that our method significantly improves cross-project defect prediction performance, compared to other methods.

Key words

software defect prediction / transfer learning / machine learning / naive Bayes

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CHENG Ming, WU Guo-qing, YUAN Meng-ting. Transfer Learning for Software Defect Prediction[J]. Acta Electronica Sinica, 2016, 44(1): 115-122. https://doi.org/10.3969/j.issn.0372-2112.2016.01.017

References

[1] Pizzi N J.A fuzzy classifier approach to estimating software quality[J].Information Sciences, 2013, 241:1-11.
[2] Jing X Y, Ying S, et al.Dictionary learning based software defect prediction[A].Proceedings of the 36th International Conference on Software Engineering[C].Hyderabad:ACM, 2014.414-423.
[3] Wang J, Shen B J, Chen Y T.Compressed C4.5 models for software defect prediction[A].Proceedings of the 12th International Conference on Quality Software[C].Xi'an:IEEE, 2012.13-16.
[4] 姜慧研, 宗茂, 刘相莹.基于ACO-SVM的软件缺陷预测模型的研究[J].计算机学报, 2011, 34(6):1148-1154. Jiang Hui-yan, Zong Mao, Liu Xiang-ying.Research of software defect prediction model based on ACO-SVM[J].Chinese Journal of Computers, 2011, 34(6):1148-1154.(in Chinese)
[5] Zimmermann T, Nagappan N, et al.Cross-project defect prediction[A].Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on Foundations of Software Engineering[C].Amsterdam:ACM, 2009.91-100.
[6] Pan S J, Yang Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10):1345-1359.
[7] Turhan B, Menzies T, et al.On the relative value of cross-company and within-company data for defect prediction[J].Empirical Software Engineering, 2009, 14(5):540-578.
[8] Canfora G, Lucia A D, et al.Multi-objective cross-project defect prediction[A].Proceedings of the 6th IEEE International Conference on Software Testing, Verification and Validation[C].Luxembourg:IEEE, 2013.252-261.
[9] Nam J, Pan S J, et al.Transfer defect learning[A].Proceedings of the 35th International Conference on Software Engineering[C].San Francisco:ACM/IEEE, 2013.382-391.
[10] Ma Y, Luo G C, et al.Transfer learning for crosscompany software defect prediction[J].Information and Software Technology, 2012, 54(3):248-256.
[11] Peng L Z, Yang B, et al.Data gravitation based classification[J].Information Sciences, 2009, 179(6):809-819.
[12] Wang C, Chen Y Q.Improving nearest neighbor classification with simulated gravitational collapse[A].Proceedings of the First International Conference on Natural Computation[C].Changsha:Springer-Verlag, 2005.845-854.
[13] Frank E, Hall M, et al.Locally weighted naive Bayes[A].Proceedings of the 9th International Conference on Uncertainty in Artificial Intelligence[C].San Francisco:Morgan Kaufmann, 2003.249-256.
[14] D'Ambros M, Lanza M, et al.An extensive comparison of bug prediction approaches[A].Proceedings of the 7th IEEE Working Conference on Mining Software Repositories[C].Cape Town:IEEE, 2010.31-41.
[15] Wu R X, Zhang H Y, et al.Relink:recovering links between bugs and changes[A].Proceedings of the 19th ACM SIGSOFT Symposium and the Thirteenth European Conference on Foundations of Software Engineering[C].Szeged:ACM, 2011.15-25.
[16] Bird C, Bachmann A, et al.Fair and balanced?:bias in bug-fix datasets[A].Proceedings of the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on Foundations of Software Engineering[C].Amsterdam:ACM, 2009.121-130.

Funding

National Natural Science Foundation of China (No.91118003, No.61003071); Special Fund for the Development of Strategic Emerging Industries in Shenzhen,  Guangdong Province (No.JCYJ20120616135936123)
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