A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

Автор: Faseeha Matloob, Shabib Aftab, Ahmed Iqbal

Журнал: International Journal of Modern Education and Computer Science @ijmecs

Статья в выпуске: 12 vol.11, 2019 года.

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Testing is one of the crucial activities of software development life cycle which ensures the delivery of high quality product. As software testing consumes significant amount of resources so, if, instead of all software modules, only those are thoroughly tested which are likely to be defective then a high quality software can be delivered at lower cost. Software defect prediction, which has now become an essential part of software testing, can achieve this goal. This research presents a framework for software defect prediction by using feature selection and ensemble learning techniques. The framework consists of four stages: 1) Dataset Selection, 2) Pre Processing, 3) Classification, and 4) Reflection of Results. The framework is implemented on six publically available Cleaned NASA MDP datasets and performance is reflected by using various measures including: F-measure, Accuracy, MCC and ROC. First the performance of all search methods within the framework on each dataset is compared with each other and the method with highest score in each performance measure is identified. Secondly, the results of proposed framework with all search methods are compared with the results of 10 well-known supervised classification techniques. The results reflect that the proposed framework outperformed all of other classification techniques.

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Feature Selection, Ensemble Classifier, Hybrid Classifier, Machine Learning Techniques, Software Defect Prediction

Короткий адрес: https://sciup.org/15017149

IDR: 15017149   |   DOI: 10.5815/ijmecs.2019.12.02

Список литературы A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • I. Gondra, “Applying machine learning to software fault-proneness prediction,” J. Syst. Softw., vol. 81, no. 2, pp. 186–195, 2008.
  • C. Manjula and L. Florence, “Deep neural network based hybrid approach for software defect prediction using software metrics,” Cluster Comput., pp. 1–17, 2018.
  • 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.
  • 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.
  • 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.
  • Y. Ma, G. Luo, X. Zeng, and A. Chen, “Transfer learning for crosscompany software defect prediction,” Information and Software Technology, vol. 54, no. 3, pp. 248–256, 2012.
  • I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
  • 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.
  • “NASA Defect Dataset.” [Online]. Available: https://github.com/klainfo/NASADefectDataset. [Accessed: 28-September-2019].
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. 44-54, 2019.
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