Abstract

A software defect is a error, failure, fault in a computer program or system producing an incorrect or unexpected result, or causing it to behave in an unintended way. Software Defect Prediction (SDP) locates defective modules in software. The final product should have null or minimal defects to produce high quality software. Software defect detection at the earliest stage reduces development cost, reworks effort and improves the quality of software. In this study, the efficiency of different classifiers such as Naïve Bayes, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are evaluated for SDP.

Keywords:

Na, K-Nearest Neighbor (KNN), Partial decision Tree Algorithm (PART), Software Defect Prediction (SDP), software quality, Support Vector Machine (SVM),

References

1. Agarwal, S. and D. Tomar, 2014. A feature selection based model for software defect prediction. Int. J. Adv. Sci. Technol., 65: 39-58.
   CrossRef    
2. Boehm, B. and V.R. Basili, 2007. Software defect reduction top 10 list. Software Eng. Barry W. Boehm's Lifetime Contribut. Software Develop. Manage. Res., 34(1): 75.
   
3. Can, H., X. Jianchun, Z. Ruide, L. Juelong, Y. Qiliang and X. Liqiang, 2013. A new model for software defect prediction using particle swarm optimization and support vector machine. Proceeding of 25th IEEE Chinese Control and Decision Conference (CCDC, 2013), pp: 4106-4110.
   CrossRef    
4. Catal, C., 2014. A comparison of semi-supervised classification approaches for software defect prediction. J. Intell. Syst., 23(1): 75-82.
   CrossRef    
5. Challagulla, V.U.B., F.B. Bastani, I.L. Yen and R.A. Paul, 2008. Empirical assessment of machine learning based software defect prediction techniques. Int. J. Artif. Intell. T., 17(02): 389-400.
   CrossRef    
6. Chug, A. and S. Dhall, 2013. Software defect prediction using supervised learning algorithm and unsupervised learning algorithm. Proceeding of 4th International Conference 2013: The Next Generation Information Technology Summit, pp: 173-179.
   CrossRef    
7. Clark, B. and D. Zubrow, 2001. How good is the software: A review of defect prediction techniques? Sponsored by the US Department of Defense. Carnegie Mellon University, Pittsburgh, PA.
   
8. Czibula, G., Z. Marian and I.G. Czibula, 2014. Software defect prediction using relational association rule mining. Inform. Sci. Int. J., 264: 260-278.
   CrossRef    
9. Gayathri, M. and A. Sudha, 2014. Software defect prediction system using multilayer perceptron neural network with data mining. Int. J. Recent Technol. Eng. (IJRTE), 3(2).
   
10. Han, J. and M. Kamber, 2006. Data Mining, Southeast Asia Edition: Concepts and Techniques. Morgan Kaufmann, pp: 770.
    
11. Khan, J.I., A.U. Gias, M. Siddik, M. Rahman, S.M. Khaled and M. Shoyaib, 2014. An attribute selection process for software defect prediction. Proceeding of the International Conference on Informatics, Electronics and Vision (ICIEV, 2014), pp: 1-4.
    CrossRef    
12. Laradji, I.H., M. Alshayeb and L. Ghouti, 2014. Software defect prediction using ensemble learning on selected features. Inform. Software Tech., 58: 388-402.
    CrossRef    
13. Liu, M., L. Miao and D. Zhang, 2014. Two-stage cost-sensitive learning for software defect prediction. IEEE T. Reliab., 63(2): 676-686.
    CrossRef    
14. Ma, Y., G. Luo, X. Zeng and A. Chen, 2012. Transfer learning for cross-company software defect prediction. Inform. Software Tech., 54(3): 248-256.
    CrossRef    
15. Nam, J., 2010. Survey on Software Defect Prediction.
    
16. Rawat, M.S. and S.K. Dubey, 2012. Software defect prediction models for quality improvement: A literature study. Int. J. Comput. Sci., 9(5).
    
17. Rawat, M.S., C.O.E.T. MGM’s, A. Mittal and S.K. Dubey, 2012. Survey on impact of software metrics on software quality. Int. J. Adv. Comput. Sci. Appl., 3(1).
    
18. Shirabad, J.S. and T.J. Menzies, 2005. The PROMISE repository of software engineering databases. School of Information Technology and Engineering, University of Ottawa, Canada.
    
19. Singh, Y., A. Kaur and R. Malhotra, 2010. Prediction of fault-prone software modules using statistical and machine learning methods. Int. J. Comput. Appl., 1(22): 8-15.
    CrossRef    
20. Song, Q., M. Shepperd, M. Cartwright and C. Mair, 2006. Software defect association mining and defect correction effort prediction. IEEE T. Software Eng., 32(2): 69-82.
    CrossRef    
21. Sridhar, S.M. and B.R. Babu, 2012. Evaluating the classification accuracy of data mining algorithms for anonymized data. Int. J. Comput. Sci. Telecommun., 3(8).
    
22. Tao, W. and L. Wei-Hua, 2010. Naïve bayes software defect prediction model. Proceeding of International Conference on Computational Intelligence and Software Engineering (CiSE), pp: 1-4.
    PMCid:PMC2848036    
23. Verner, J. and G. Tate, 1992. A software size model. IEEE T. Software Eng., 18(4): 265-278.
    CrossRef    
24. Voulgaris, Z. and G.D. Magoulas, 2008. Extensions of the k nearest neighbour methods for classification problems. Proceeding of 26th IASTED International Conference on Artificial Intelligence and Applications, CD Proceedings ISBN: 978-0-88986-710-9, pp: 23-28.
    PMCid:PMC2633014    
25. Wahono, R.S., N. Suryana and S. Ahmad, 2014. Metaheuristic optimization based feature selection for software defect prediction. J. Software, 9(5): 1324-1333.
    CrossRef    
26. Wang, J., B. Shen and Y. Chen, 2012b. Compressed C4. 5 models for software defect prediction. Proceeding of 12th International Conference on Quality Software (QSIC), pp: 13-16.
    
27. Wang, P., C. Jin and S.W. Jin, 2012a. Software defect prediction scheme based on feature selection. Proceeding of International Symposium on Information Science and Engineering (ISISE, 2012), pp: 477-480.
    CrossRef    
28. Xia, Y., G. Yan and Q. Si, 2013. A study on the significance of software metrics in defect prediction. Proceeding of 6th International Symposium on Computational Intelligence and Design (ISCID, 2013), 2: 343-346.
    
29. Xing, F., P. Guo and M.R. Lyu, 2005. A novel method for early software quality prediction based on support vector machine. Proceeding of 16th IEEE International Symposium on Software Reliability Engineering (ISSRE, 2005), pp: 10.
    CrossRef    
30. Zheng, J., 2010. Cost-sensitive boosting neural networks for software defect prediction. Expert Syst. Appl., 37(6): 4537-4543.
    CrossRef    

Competing interests

The authors have no competing interests.

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