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International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.12, No.5, Oct. 2020

Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques

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Author(s)

Umair Ali, Shabib Aftab, Ahmed Iqbal, Zahid Nawaz, Muhammad Salman Bashir, Muhammad Anwaar Saeed

Index Terms

Software Defect Prediction, Feature Selection, Classifier Variant, Ensemble Learning. Machine Learning Techniques

Abstract

Testing is considered as one of the expensive activities in software development process. Fixing the defects during testing process can increase the cost as well as the completion time of the project. Cost of testing process can be reduced by identifying the defective modules during the development (before testing) stage. This process is known as “Software Defect Prediction”, which has been widely focused by many researchers in the last two decades. This research proposes a classification framework for the prediction of defective modules using variant based ensemble learning and feature selection techniques. Variant selection activity identifies the best optimized versions of classification techniques so that their ensemble can achieve high performance whereas feature selection is performed to get rid of such features which do not participate in classification and become the cause of lower performance. The proposed framework is implemented on four cleaned NASA datasets from MDP repository and evaluated by using three performance measures, including: F-measure, Accuracy, and MCC. According to results, the proposed framework outperformed 10 widely used supervised classification techniques, including: “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)”.

Cite This Paper

Umair Ali, Shabib Aftab, Ahmed Iqbal, Zahid Nawaz, Muhammad Salman Bashir, Muhammad Anwaar Saeed, " Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques", International Journal of Modern Education and Computer Science(IJMECS), Vol.12, No.5, pp. 29-40, 2020.DOI: 10.5815/ijmecs.2020.05.03

Reference

[1]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.

[2]A. Iqbal, S. Aftab, I. Ullah, M. S. Bashir, and M. A. Saeed, “A Feature Selection based Ensemble Classification Framework for Software Defect Prediction,” Int. J. Mod. Educ. Comput. Sci., vol. 11, no. 9, pp. 54-64, 2019.

[3]A. Iqbal, S. Aftab, and F. Matloob, “Performance Analysis of Resampling Techniques on Class Imbalance Issue in Software Defect Prediction,” Int. J. Inf. Technol. Comput. Sci., vol. 11, no. 11, pp. 44-53, 2019

[4]F. Matloob, S. Aftab, and A. Iqbal, “A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques,” Int. J. Mod. Educ. Comput. Sci., vol. 11, no. 12, pp. 14-20, 2019.

[5]A. Iqbal, and S. Aftab, “A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP,” Int. J. Mod. Educ. Comput. Sci., vol. 12, no. 1, pp. 18-25, 2020.

[6]A. Dadwal, H. Washizaki, Y. Fukazawa, T. Iida, M. Mizoguchi, and K. Yoshimura, “Prioritization in automotive software testing: Systematic literature review,” CEUR Workshop Proc., vol. 2273, no. QuASoQ, pp. 52–58, 2018.

[7]A. Bertolino, “Software testing research: Achievements, challenges, dreams,” FoSE 2007 Futur. Softw. Eng., no. September, pp. 85–103, 2007.

[8]R. M. De Castro Andrade, I. De Sousa Santos, V. Lelli, Ḱathia Marçal De Oliveira, and A. R. Rocha, “Software testing process in a test factory from ad hoc activities to an organizational standard,” ICEIS 2017 - Proc. 19th Int. Conf. Enterp. Inf. Syst., vol. 2, no. Iceis, pp. 132–143, 2017.

[9]D. Kumar and K. K. Mishra, “The Impacts of Test Automation on Software’s Cost, Quality and Time to Market,” Procedia Comput. Sci., vol. 79, pp. 8–15, 2016.

[10]S. Huda et al., “A Framework for Software Defect Prediction and Metric Selection,” IEEE Access, vol. 6, no. c, pp. 2844–2858, 2017

[11]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.

[12]Y. Ma, G. Luo, X. Zeng, and A. Chen, “Transfer learning for cross-company software defect prediction,” Inf. Softw. Technol., vol. 54, no. 3, Mar. 2012.

[13]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.

[14]M. Ahmad, S. Aftab, S. S. Muhammad, and S. Ahmad, “Machine Learning Techniques for Sentiment Analysis: A Review,” Int. J. Multidiscip. Sci. Eng., vol. 8, no. 3, p. 27, 2017.

[15]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.

[16]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.

[17]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.

[18]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.

[19]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.

[20]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.

[21]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.

[22]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.

[23]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.

[24]“NASA Defect Dataset.” [Online]. Available: https://github.com/klainfo/NASADefectDataset. [Accessed: 27-October-2019].

[25]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.

[26]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.

[27]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.

[28]A. O. Balogun, S. Basri, S. J. Abdulkadir, and A. S. Hashim, “Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach,” Appl. Sci., vol. 9, no. 13, p. 2764, 2019.

[29]N. Sánchez-Maroño, A. Alonso-Betanzos, and M. Tombilla-Sanromán, “Filter methods for feature selection - A comparative study,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 4881 LNCS, pp. 178–187, 2007.

[30]M. R. Malik, L. Yining, and S. Shaikh, “Analysis of Software Deformity Prone Datasets with Use of Attribute Selected Classifier,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 7, pp. 14–21, 2019

[31]U. R. Salunkhe and S. N. Mali, “A hybrid approach for class imbalance problem in customer churn prediction: A novel extension to under-sampling,” Int. J. Intell. Syst. Appl., vol. 10, no. 5, pp. 71–81, 2018.

[32]N. F. Hordri, S. S. Yuhaniz, N. F. M. Azmi, and S. M. Shamsuddin, “Handling class imbalance in credit card fraud using resampling methods,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 11, pp. 390–396, 2018.