Main Article Content

Lilly Florence


Abstract: Software defect prediction is considered as most interesting area for researchers in the field of software engineering. This process of defect prediction, identifies the bug during automated testing process which prevents the development of faulty software module. According to this process, previous archives of software modules are considered for analyzing the quality based on data mining and machine leaning concepts, which identifies the faults in software modules. Several techniques have been presented for software defect prediction using data mining techniques and machine learning techniques but achieving desired accuracy in performance is a challenging task for researchers. To address this issue, in this work we have presented a new approach for software defect prediction by combining genetic algorithm optimization process for feature subspace reduction and deep belief network for pattern learning. Deep belief networks are further improved by applying L1-regularization scheme resulting in better learning process by reducing the overfitting error. This combined model is implemented on SPIE lab software defect database. An extensive experimental study is carried out which shows that proposed approach achieves higher accuracy when compared with state-of-the-art software defect prediction techniques.


Download data is not yet available.

Article Details




Hryszko, Jaroslaw, and Lech Madeyski. "Assessment of the Software Defect Prediction Cost Effectiveness in an Industrial Project." In Software Engineering: Challenges and Solutions, pp. 77-90. Springer International Publishing, 2017.

He, P., Li, B., Liu, X., Chen, J. and Ma, Y., 2015. An empirical study on software defect prediction with a simplified metric set. Information and Software Technology, 59, pp.170-190.

Arora, I. and Saha, A., 2018. Software Defect Prediction: A Comparison Between Artificial Neural Network and Support Vector Machine. In Advanced Computing and Communication Technologies (pp. 51-61). Springer, Singapore.

Mohanty, R. and Ravi, V., 2017. Machine Learning Techniques to Predict Software Defect. In Artificial Intelligence: Concepts, Methodologies, Tools, and Applications (pp. 1473-1487). IGI Global.

Moussa, R. and Azar, D., 2017. A PSO-GA approach targeting fault-prone software modules. Journal of Systems and Software, 132, pp.41-49.

Liu, M., Miao, L. and Zhang, D., 2014. Two-stage cost-sensitive learning for software defect prediction. IEEE Transactions on Reliability, 63(2), pp.676-686.

Ryu, D., Jang, J.I. and Baik, J., 2015. A hybrid instance selection using nearest-neighbor for cross-project defect prediction. Journal of Computer Science and Technology, 30(5), pp.969-980.

Shepperd, M., Song, Q., Sun, Z. and Mair, C., 2013. Data quality: Some comments on the nasa software defect datasets. IEEE Transactions on Software Engineering, 39(9), pp.1208-1215.

Arar, Ö.F. and Ayan, K., 2017. A Feature Dependent Naive Bayes Approach and Its Application to the Software Defect Prediction Problem. Applied Soft Computing.

Quah, T.S. and Thwin, M.M.T., 2003, September. Application of neural networks for software quality prediction using object-oriented metrics. In Software Maintenance, 2003. ICSM 2003. Proceedings. International Conference on (pp. 116-125). IEEE.

Kanmani, S., Uthariaraj, V.R., Sankaranarayanan, V. and Thambidurai, P., 2007. Object-oriented software fault prediction using neural networks. Information and software technology, 49(5), pp.483-492.

S. Lessmann, B. Baesens, C. Mues and S. Pietsch, "Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings," in IEEE Transactions on Software Engineering, vol. 34, no. 4, pp. 485-496, July-Aug. 2008.

Chen, X., Shen, Y., Cui, Z. and Ju, X., 2017, July. Applying Feature Selection to Software Defect Prediction Using Multi-objective Optimization. In Computer Software and Applications Conference (COMPSAC), 2017 IEEE 41st Annual (Vol. 2, pp. 54-59). IEEE.

Hosseini, S., Turhan, B. and Mäntylä, M., 2017. A benchmark study on the effectiveness of search-based data selection and feature selection for cross project defect prediction. Information and Software Technology.

Sabharwal, S., Nagpal, S., Malhotra, N., Singh, P. and Seth, K., 2018. Analysis of Feature Ranking Techniques for Defect Prediction in Software Systems. In Quality, IT and Business Operations (pp. 45-56). Springer, Singapore.

Maua, G. and GalinacGrbac, T., 2017. Co-evolutionary multi-population genetic programming for classification in software defect prediction. Applied Soft Computing, 55(C), pp.331-351.

Afzal, W. and Torkar, R., 2016. Towards benchmarking feature subset selection methods for software fault prediction. In Computational Intelligence and Quantitative Software Engineering (pp. 33-58). Springer International Publishing.

Li, J., He, P., Zhu, J. and Lyu, M.R., 2017, July. Software Defect Prediction via Convolutional Neural Network. In Software Quality, Reliability and Security (QRS), 2017 IEEE International Conference on (pp. 318-328). IEEE.

Wang, T., Zhang, Z., Jing, X. and Zhang, L., 2016. Multiple kernel ensemble learning for software defect prediction. Automated Software Engineering, 23(4), pp.569-590.

Wang, S., Ping, H. and Zelin, L., 2016. An enhanced software defect prediction model with multiple metrics and learners. International Journal of Industrial and Systems Engineering, 22(3), pp.358-371.

Tomar, D. and Agarwal, S., 2016. Prediction of defective software modules using class imbalance learning. Applied Computational Intelligence and Soft Computing, 2016, p.6.

Hryszko, J. and Madeyski, L., 2017. Assessment of the Software Defect Prediction Cost Effectiveness in an Industrial Project. In Software Engineering: Challenges and Solutions (pp. 77-90). Springer International Publishing.

D'Ambros, M., Lanza, M. and Robbes, R., 2010, May. An extensive comparison of bug prediction approaches. In Mining Software Repositories (MSR), 2010 7th IEEE Working Conference on (pp. 31-41). IEEE.

Zhang, F., Mockus, A., Keivanloo, I. and Zou, Y., 2014, May. Towards building a universal defect prediction model. In Proceedings of the 11th Working Conference on Mining Software Repositories (pp. 182-191). ACM.