Abstract
Cross-project defect prediction (CPDP) is a technique of detecting defects in software modules in which the training and the testing projects for the classification model are different. The effective prediction leads to a more reliable software. The merging of dataset from varying sources results to an imbalanced dataset. The complex structure and the imbalance data make it a challenge for an effective cross-project defect prediction. To overcome these issues, in this paper, we propose a cross-project defect prediction framework. In the first stage of this framework, PCA is applied for dimensionality reduction of the dataset into two components. In the second phase, SMOTE technique of data sampling is applied to handle the class imbalance problem. Then the ensemble classifiers random forest and XGBoost are applied for an effective defect-prediction model. We have conducted the experiments on eight open source software projects. The results are compared with few baseline techniques. The results indicate that the proposed framework gave comparable performance of cross-project defect prediction to some baseline methods.
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References
Caglayan B, Misirli AT, Bener AB, Miranskyy A (2015) Predicting defective modules in different test phases. Softw Quality J 23(2):205–227
Monden A et al (2013) Assessing the cost effectiveness of fault prediction in acceptance testing. IEEE Trans Softw Eng 39(10):1345–1357
Hall T, Beecham S, Bowes D, Gray D, Counsell S (2012) A systematic literature review on fault prediction performance in software engineering. IEEE Trans Softw Eng 38(6):1276–1304
Kitchenham BA, Mendes E, Travassos GH (2007) Cross versus within company cost estimation studies: systematic review. IEEE Trans Softw Eng 33(5):316329
Anushree A, Malhotra R (2019) Cross project defect prediction for open source software. Int J Inf Technol 1–15. https://doi.org/10.1007/s41870-019-00299-6
Turhan B, Menzies T, Bener AB, Di Stefano J (2009) On the relative value of cross-company and within-company data for defect prediction. Empirical Software Eng 14(5):540–578
Zimmermann T, Nagappan N, Gall H, Giger E, Murphy B (2009) Cross-project defect prediction. In: Proceedings of the 7th joint meeting of the european software engineering conference and the ACM SIGSOFT symposium on the foundations of software engineering (ESEC/FSE), pp 91–100
Ma Y, Luo G, Zeng X, Chen A (2012) Transfer learning for cross-company software defect prediction. Inf Softw Technol 54(3):248–256
Nam J, Pan SJ, Kim S (2013) Transfer defect learning. In: Proceedings of the 35th international conference on software engineering (ICSE), pp 382–391
Herbold S (2013) Training data selection for cross-project defect prediction. In: Proceedings of the 9th international conference on predictive models in software engineering (PROMISE), pp 6–16
Jing XY, Wu F, Dong X, Qi F, Xu B (2015) Heterogeneous cross company defect prediction by unified metric representation and CCA-based transfer learning. In: Proceedings of the 10th joint meeting on foundations of software engineering, pp 496–507
Nam J, Kim S (2015) Heterogeneous defect prediction. In: Proceeding ESEC/FSE 2015 proceedings of the 2015 10th joint meeting on foundations of software engineering, ACM. New York, pp 508–519. https://doi.org/10.1145/2786805.2786814
Lin C, Fang B, Shang Z, Tang Y (2015) Negative samples reduction in cross-company software defects prediction. Inf Softw Technol 62:67–77
Ryu D, Choi O, Baik J (2016) Value-cognitive boosting with a support vector machine for crossproject defect prediction. Empirical Softw Eng. https://doi.org/10.1007/s10664-014-9346-4
Zhang, F et al. (2016) Cross-project defect prediction using a connectivity-based unsupervised classifier. In: Proceedings of the 38th international conference on software engineering. ACM
Xia X et al (2016) Hydra: massively compositional model for cross-project defect prediction. IEEE Trans Softw Eng 42(10):977–998
Herbold S et al (2017) A comparative study to benchmark cross-project defect prediction approaches. IEEE Trans Softw Eng
Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
He P, He Y, Yu L, Li B (2018) An improved method for cross-project defect prediction by simplifying training data. Math Probl Eng 18, Article ID 2650415
Limsettho N, Bennin, K, Keung J, Hata H, Matsumoto K (2018) Cross project defect prediction using class distribution estimation and oversampling. Inf Softw Technol 100. https://doi.org/10.1016/j.infsof.2018.04.001
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Goel, L., Sharma, M., Khatri, S.K., Damodaran, D. (2020). Defect Prediction of Cross Projects Using PCA and Ensemble Learning Approach. In: Sharma, D.K., Balas, V.E., Son, L.H., Sharma, R., Cengiz, K. (eds) Micro-Electronics and Telecommunication Engineering. Lecture Notes in Networks and Systems, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-15-2329-8_31
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DOI: https://doi.org/10.1007/978-981-15-2329-8_31
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