When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies.
References
[1]
Murphy, K.P. (2012) Machine Learning: A Probabilistic Perspective. MIT Press, Cambridge.
[2]
Dietterich, T.G. (2000) Ensemble Methods in Machine Learning. In: International Workshop on Multiple Classifier Systems, Springer, Berlin, 1-15. https://doi.org/10.1007/3-540-45014-9_1
[3]
Cristianini, N. and De Bie, T. (2005) Support Vector Machines. Hodder Arnold, London.
[4]
Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
[5]
Chen, T. and Guestrin, C. (2016) Xgboost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, 13-17 August 2016, 785-794. https://doi.org/10.1145/2939672.2939785
[6]
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning. Vol. 112, Springer, New York, 18. https://doi.org/10.1007/978-1-4614-7138-7
[7]
Friedman, J.H. (2001) Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29, 1189-1232. https://doi.org/10.1214/aos/1013203451
[8]
Podgorelec, V., Kokol, P., Stiglic, B. and Rozman, I. (2002) Decision Trees: An Overview and Their Use in Medicine. Journal of Medical Systems, 26, 445-463. https://doi.org/10.1023/A:1016409317640
[9]
Bishop, C.M. (2006) Pattern Recognition and Machine Learning by Christopher M. Bishop. Springer Science Business Media, Berlin.
[10]
Hastie, T., Tibshirani, R., Friedman, J.H. and Friedman, J.H. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol. 2, Springer, New York, 1-758. https://doi.org/10.1007/978-0-387-84858-7
[11]
Burges, C.J. (1998) A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 2, 121-167. https://doi.org/10.1023/A:1009715923555
Fawcett, T. (2006) An Introduction to ROC Analysis. Pattern Recognition Letters, 27, 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
[14]
Thota, M.K., Shajin, F.H. and Rajesh, P. (2020) Survey on Software Defect Prediction Techniques. International Journal of Applied Science and Engineering, 17, 331-344.
[15]
Li, N., Shepperd, M. and Guo, Y. (2020) A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction. Information and Software Technology, 122, Article ID: 106287. https://doi.org/10.1016/j.infsof.2020.106287
[16]
Matloob, F., Ghazal, T.M., Taleb, N., Aftab, S., Ahmad, M., Khan, M.A. and Soomro, T.R. (2021) Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review.IEEE Access, 9, 98754-98771. https://doi.org/10.1109/ACCESS.2021.3095559
[17]
Akimova, E.N., Bersenev, A.Y., Deikov, A.A., Kobylkin, K.S., Konygin, A.V., Mezentsev, I.P. and Misilov, V.E. (2021) A Survey on Software Defect Prediction Using Deep Learning. Mathematics, 9, Article No. 1180. https://doi.org/10.3390/math9111180
[18]
Gong, L., Rajbahadur, G.K., Hassan, A.E. and Jiang, S. (2021) Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction. IEEE Transactions on Software Engineering, 48, 5030-5049. https://doi.org/10.1109/TSE.2021.3131950
[19]
Khan, M.A., Elmitwally, N.S., Abbas, S., Aftab, S., Ahmad, M., Fayaz, M. and Khan, F. (2022) Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review. Scientific Programming, 2022, Article ID: 2117339. https://doi.org/10.1155/2022/2117339
[20]
Goyal, S. (2022) Effective Software Defect Prediction Using Support Vector Machines (SVMs). International Journal of System Assurance Engineering and Management, 13, 681-696. https://doi.org/10.1007/s13198-021-01326-1
[21]
Uddin, M.N., Li, B., Ali, Z., Kefalas, P., Khan, I. and Zada, I. (2022) Software Defect Prediction Employing BiLSTM and BERT-Based Semantic Feature. Soft Computing,26, 7877-7891. https://doi.org/10.1007/s00500-022-06830-5
[22]
Zhao, Y., Damevski, K. and Chen, H. (2023) A Systematic Survey of Just-in-Time Software Defect Prediction. ACM Computing Surveys, 55, 1-35. https://doi.org/10.1145/3567550
[23]
Giray, G., Bennin, K.E., Köksal, Ö., Babur, Ö. and Tekinerdogan, B. (2023) On the Use of Deep Learning in Software Defect Prediction. Journal of Systems and Software,195, Article ID: 111537. https://doi.org/10.1016/j.jss.2022.111537
[24]
Stradowski, S. and Madeyski, L. (2023) Machine Learning in Software Defect Prediction: A Business-Driven Systematic Mapping Study. Information and Software Technology, 155, Article ID: 107128. https://doi.org/10.1016/j.infsof.2022.107128
Elish, K.O. and Elish, M.O. (2008) Predicting Defect-Prone Software Modules Using Support Vector Machines. Journal of Systems and Software, 81, 649-660. https://doi.org/10.1016/j.jss.2007.07.040
[27]
Kim, S., Zhang, H., Wu, R. and Gong, L. (2011) Dealing with Noise in Defect Prediction. Proceedings of the 33rd International Conference on Software Engineering,Honolulu, 21-28 May 2011, 481-490. https://doi.org/10.1145/1985793.1985859
[28]
Lee, T., Nam, J., Han, D., Kim, S. and In, H.P. (2011) Micro Interaction Metrics for Defect Prediction. Proceedings of the 19th ACM SIGSOFT Symposium and the 13th European Conference on Foundations of Software Engineering, September 2011, 311-321. https://doi.org/10.1145/2025113.2025156
