International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 11, No. 9, 8 Sep. 2019
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A Feature Selection based Ensemble Classification Framework for Software Defect Prediction
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Author(s)
Ahmed Iqbal
1,*
Shabib Aftab
1
Israr Ullah
1
Muhammad Salman Bashir
1
Muhammad Anwaar Saeed
1
Index Terms
Ensemble Classifier, Hybrid Classifier, Random Forest, Software Defect Prediction, Feature Selection
Abstract
Software defect prediction is one of the emerging research areas of software engineering. The prediction of defects at early stage of development process can produce high quality software at lower cost. This research contributes by presenting a feature selection based ensemble classification framework which consists of four stages: 1) Dataset selection, 2) Feature Selection, 3) Classification, and 4) Results. The proposed framework is implemented from two dimensions, one with feature selection and second without feature selection. The performance is evaluated through various measures including: Precision, Recall, F-measure, Accuracy, MCC and ROC. 12 Cleaned publically available NASA datasets are used for experiments. The results of both the dimensions of proposed framework are compared with the other widely used classification techniques such as: “Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF)”. Results reflect that the proposed framework outperformed other classification techniques in some of the used datasets however class imbalance issue could not be fully resolved.
Cite This Paper
Ahmed Iqbal, Shabib Aftab, Israr Ullah, Muhammad Salman Bashir, Muhammad Anwaar Saeed, "A Feature Selection based Ensemble Classification Framework for Software Defect Prediction", International Journal of Modern Education and Computer Science(IJMECS), Vol.11, No.9, pp. 54-64, 2019.DOI: 10.5815/ijmecs.2019.09.06
Reference
[1]S. Huda, S. Alyahya, M. M. Ali, S. Ahmad, J. Abawajy, J. Al-Dossari, and J. Yearwood, “A Framework for Software Defect Prediction and Metric Selection,” IEEE Access, vol. 6, pp. 2844–2858, 2018.
[2]E. Erturk and E. Akcapinar, “A comparison of some soft computing methods for software fault prediction,” Expert Syst. Appl., vol. 42, no. 4, pp. 1872–1879, 2015.
[3]Y. Ma, G. Luo, X. Zeng, and A. Chen, “Transfer learning for crosscompany software defect prediction,” Inf. Softw. Technol., vol. 54, no. 3, Mar. 2012.
[4]M. Shepperd, Q. Song, Z. Sun and C. Mair, “Data Quality: Some Comments on the NASA Software Defect Datasets,” IEEE Trans. Softw. Eng., vol. 39, pp. 1208–1215, 2013.
[5]I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
[6]“NASA Defect Dataset.” [Online]. Available: https://github.com/klainfo/NASADefectDataset. [Accessed: 01-July-2019].
[7]B. Ghotra, S. McIntosh, and A. E. Hassan, “Revisiting the impact of classification techniques on the performance of defect prediction models,” Proc. - Int. Conf. Softw. Eng., vol. 1, pp. 789–800, 2015.
[8]G. Czibula, Z. Marian, and I. G. Czibula, “Software defect prediction using relational association rule mining,” Inf. Sci. (Ny)., vol. 264, pp. 260–278, 2014.
[9]D. Rodriguez, I. Herraiz, R. Harrison, J. Dolado, and J. C. Riquelme, “Preliminary comparison of techniques for dealing with imbalance in software defect prediction,” in Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering. ACM, p. 43, 2014.
[10]A. Iqbal, S. Aftab, U. Ali, Z. Nawaz, L. Sana, M. Ahmad, and A. Husen “Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 5, 2019.
[11]M. Ahmad, S. Aftab, I. Ali, and N. Hameed, “Hybrid Tools and Techniques for Sentiment Analysis: A Review,” Int. J. Multidiscip. Sci. Eng., vol. 8, no. 3, 2017.
[12]M. Ahmad, S. Aftab, and S. S. Muhammad, “Machine Learning Techniques for Sentiment Analysis: A Review,” Int. J. Multidiscip. Sci. Eng., vol. 8, no. 3, p. 27, 2017.
[13]M. Ahmad, S. Aftab, and I. Ali, “Sentiment Analysis of Tweets using SVM,” Int. J. Comput. Appl., vol. 177, no. 5, pp. 25–29, 2017.
[14]M. Ahmad and S. Aftab, “Analyzing the Performance of SVM for Polarity Detection with Different Datasets,” Int. J. Mod. Educ. Comput. Sci., vol. 9, no. 10, pp. 29–36, 2017.
[15]M. Ahmad, S. Aftab, M. S. Bashir, N. Hameed, I. Ali, and Z. Nawaz, “SVM Optimization for Sentiment Analysis,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 4, 2018.
[16]M. Ahmad, S. Aftab, M. S. Bashir, and N. Hameed, “Sentiment Analysis using SVM: A Systematic Literature Review,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 2, 2018.
[17]A. Iqbal and S. Aftab, “A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection,” Int. J. Comput. Netw. Inf. Secur., vol. 11, no. 4, pp. 19–25, 2019.
[18]A. Iqbal, S. Aftab, I. Ullah, M. A. Saeed, and A. Husen, “A Classification Framework to Detect DoS Attacks,” Int. J. Comput. Netw. Inf. Secur., vol. 11, no. 9, pp. 40-47, 2019.
[19]S. Behal, K. Kumar, and M. Sachdeva, “D-FAC: A novel ϕ-Divergence based distributed DDoS defense system,” J. King Saud Univ. - Comput. Inf. Sci., 2018.
[20]S. Aftab, M. Ahmad, N. Hameed, M. S. Bashir, I. Ali, and Z. Nawaz, “Rainfall Prediction in Lahore City using Data Mining Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 4, 2018.
[21]S. Aftab, M. Ahmad, N. Hameed, M. S. Bashir, I. Ali, and Z. Nawaz, “Rainfall Prediction using Data Mining Techniques: A Systematic Literature Review,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 5, 2018.
[22]S. Wang and X. Yao, “Using class imbalance learning for software defect prediction,” IEEE Transactions on Reliability, vol. 62, no. 2, pp. 434–443, 2013.
[23]J. C. Riquelme, R. Ruiz, D. Rodr´ıguez, and J. Moreno, “Finding defective modules from highly unbalanced datasets,” Actas de los Talleres de las Jornadas de Ingenier´ıa del Software y Bases de Datos, vol. 2, no. 1, pp. 67–74, 2008
[24]F. Lanubile, A. Lonigro, and G. Visaggio, “Comparing Models for Identifying Fault-Prone Software Components,” Proc. Seventh Int’l Conf. Software Eng. and Knowledge Eng., pp. 312–319, June 1995.
[25]K. O. Elish and M. O. Elish, “Predicting defect-prone software modules using support vector machines,” J. Syst. Softw., vol. 81, no. 5, pp. 649–660, 2008.
[26]I. Gondra, “Applying machine learning to software fault-proneness prediction,” J. Syst. Softw., vol. 81, no. 2, pp. 186–195, 2008.
[27]O. F. Arar and K. Ayan, “Software defect prediction using cost-sensitive ¨ neural network,” Applied Soft Computing, vol. 33, pp. 263–277, 2015.
[28]C. Manjula and L. Florence, “Deep neural network based hybrid approach for software defect prediction using software metrics,” Cluster Comput., pp. 1–17, 2018.
[29]R. Moser, W. Pedrycz, and G. Succi, “A Comparative Analysis of the Efficiency of Change Metrics and Static Code Attributes for Defect Prediction”, In Robby, editor, ICSE, pp. 181–190. ACM, 2008.
[30]E. Giger, M. D’Ambros, M. Pinzger, and H. C. Gall, “Method-level bug prediction,” in ESEM ’12, pp. 171–180, 2012.
[31]S. E. S. Taba, F. Khomh, Y. Zou, A. E. Hassan, and M. Nagappan, “Predicting bugs using antipatterns,” in Proc. of the 29th Int’l Conference on Software Maintenance, pp. 270–279, 2013.
[32]K. Herzig, S. Just, A. Rau, and A. Zeller, “Predicting defects using change genealogies,” in Software Reliability Engineering (ISSRE), 2013 IEEE 24th International Symposium on, pp. 118–127, 2013.