Cluster Computing

, Volume 22, Supplement 1, pp 77–88 | Cite as

Software defect prediction techniques using metrics based on neural network classifier

  • R. JayanthiEmail author
  • Lilly Florence


Software industries strive for software quality improvement by consistent bug prediction, bug removal and prediction of fault-prone module. This area has attracted researchers due to its significant involvement in software industries. Various techniques have been presented for software defect prediction. Recent researches have recommended data-mining using machine learning as an important paradigm for software bug prediction. state-of-art software defect prediction task suffer from various issues such as classification accuracy. However, software defect datasets are imbalanced in nature and known fault prone due to its huge dimension. To address this issue, here we present a combined approach for software defect prediction and prediction of software bugs. Proposed approach delivers a concept of feature reduction and artificial intelligence where feature reduction is carried out by well-known principle component analysis (PCA) scheme which is further improved by incorporating maximum-likelihood estimation for error reduction in PCA data reconstruction. Finally, neural network based classification technique is applied which shows prediction results. A framework is formulated and implemented on NASA software dataset where four datasets i.e., KC1, PC3, PC4 and JM1 are considered for performance analysis using MATLAB simulation tool. An extensive experimental study is performed where confusion, precision, recall, classification accuracy etc. parameters are computed and compared with existing software defect prediction techniques. Experimental study shows that proposed approach can provide better performance for software defect prediction.


Defect prediction models Machine learning techniques Software defect prediction Software metrics 



I would like to express my gratitude to Dr. Lilly Florence Prof. & HOD, MCA programme, Adhiyamaan College of Engineering, Hosur for providing me the guidance and support to make this work possible.


  1. 1.
    Tian, J.: Software Quality Engineering: Testing, Quality Assurance, and Quantifiable Improvement. Wiley, Hoboken (2005)CrossRefGoogle Scholar
  2. 2.
    Salfner, F., Lenk, M., Malek, M.: A survey of online failure prediction methods. ACM Comput. Surv. 42(3), 10 (2010). CrossRefGoogle Scholar
  3. 3.
  4. 4.
    Chauhan, N.S., Saxena, A.: A green software development life cycle for cloud computing. IT Prof. 15(1), 28–34 (2013)CrossRefGoogle Scholar
  5. 5.
    Sandhu, P.S., Brar, A.S., Goel, R., Kaur, J., Anand, S.: A model for early prediction of faults in software systems. In: 2nd International Conference on Computer and Automation Engineering, Singapore, pp. 281–285 (2010)Google Scholar
  6. 6.
    Emam, K.E., Melo, W., Machado, J.C.: The prediction of faulty classes using object-oriented design metrics. J. Syst. Softw. 56, 63–75 (2001)CrossRefGoogle Scholar
  7. 7.
    Kuncheva, L.I., Skurichina, M., Duin, R.P.W.: An experimental study on diversity for bagging and boosting with linear classifiers. Inf. Fus. 3(4), 245–258 (2002)CrossRefGoogle Scholar
  8. 8.
    Aljamaan, H.I., Elish, M.O.: An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software. In: Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining (CIDM ’09), pp. 187–194, IEEE, Nashville (2009)Google Scholar
  9. 9.
    Okutan, A., Yıldız, O.T.: Software defect prediction using Bayesian networks. Empir. Softw. Eng. 19(1), 154–181 (2014)CrossRefGoogle Scholar
  10. 10.
    Shan, C., Chen, B., Hu, C., Xue, J., Li, N.: Software defect prediction model based on LLE and SVM. In: Proceedings of the Communications Security Conference (CSC ’14), pp. 1–5 (2014)Google Scholar
  11. 11.
    Koru, A.G., Liu, H.: Building effective defect-prediction models in practice. IEEE Softw. 22(6), 23–29 (2005)CrossRefGoogle Scholar
  12. 12.
    Sheela, K.G., Deepa, S.N.: Neural network based hybrid computing model for wind speed prediction. Neurocomputing 122, 425–429 (2013)CrossRefGoogle Scholar
  13. 13.
    Wang, T., Zhang, Z., Jing, X., Zhang, L.: Multiple kernel ensemble learning for software defect prediction. Autom. Softw. Eng. 23, 569–590 (2015)CrossRefGoogle Scholar
  14. 14.
    Xu, Z., Xuan, J., Liu, J., Cui, X.: MICHAC: defect prediction via feature selection based on maximal information coefficient with hierarchical agglomerative clustering. In: 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), Suita, pp. 370–381 (2016)Google Scholar
  15. 15.
    Ryu, D., Baik, J.: Effective multi-objective naïve Bayes learning for cross-project defect prediction. Appl. Soft Comput. 49, 1062 (2016). CrossRefGoogle Scholar
  16. 16.
    Abdi, Y., Parsa, S., Seyfari, Y.: A hybrid one-class rule learning approach based on swarm intelligence for software fault prediction. Innov. Syst. Softw. Eng. 11(4), 289–301 (2015). CrossRefGoogle Scholar
  17. 17.
    Valles-Barajas, F.: A comparative analysis between two techniques for the prediction of software defects: fuzzy and statistical linear regression. Innov. Syst. Softw. Eng. 11(4), 277–287 (2015). CrossRefGoogle Scholar
  18. 18.
    Shan C., Chen B., Hu C., Xue J., Li N.: Software defect prediction model based on LLE and SVM. In: Proceedings of the Communications Security Conference (CSC ’14), pp. 1–5 (2014)Google Scholar
  19. 19.
    Yang, Z.R.: A novel radial basis function neural network for discriminant analysis. IEEE Trans. Neural Netw. 17(3), 604–612 (2006). CrossRefGoogle Scholar
  20. 20.
    Arar, Ö.F., Ayan, K.: Software defect prediction using cost-sensitive neural network. Appl. Soft Comput. J. 33, 263–277 (2015)CrossRefGoogle Scholar
  21. 21.
    Bautista, A.M., Feliu, T.S.: Defect prediction in software repositories with artificial neural networks. In: Mejia, J., Munoz, M., Rocha, Á., Calvo-Manzano, J. (eds.) Trends and Applications in Software Engineering. Advances in Intelligent Systems and Computing, vol. 405. Springer, Cham (2016)Google Scholar
  22. 22.
    Khoshgoftaar, T.M., Gao, K.: Feature selection with imbalanced data for software defect prediction. In: 2009 International Conference on Machine Learning and Applications, Miami Beach, pp. 235–240 (2009)Google Scholar
  23. 23.
    Khoshgoftaar, T.M., Seliya, N., Sundaresh, N.: An empirical study of predicting software faults with case-based reasoning. Softw. Qual. J. 14(2), 85–111 (2006)CrossRefGoogle Scholar
  24. 24.
    Malhi, A.: PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas. 53(6), 1517–1525 (2004)CrossRefGoogle Scholar
  25. 25.
    Clark, C.C.T., et al.: A review of emerging analytical techniques for objective physical activity measurement in humans. Sports Med. 47, 439–447 (2016)CrossRefGoogle Scholar
  26. 26.
    Software Defect Dataset: Promise repository,
  27. 27.
    Andersson, C.: A replicated empirical study of a selection method for software reliability growth models. Empir. Softw. Eng. 12(2), 161–182 (2007)CrossRefGoogle Scholar
  28. 28.
    Andersson, C., Runeson, P.: A replicated quantitative analysis of fault distributions in complex software systems. IEEE Trans. Softw. Eng. 33(5), 273–286 (2007)CrossRefGoogle Scholar
  29. 29.
    Mangasarian, O.L., Musicant, D.R.: Lagrangian support vector machines. J. Mach. Learn. Res. 1, 161–177 (2001)MathSciNetzbMATHGoogle Scholar
  30. 30.
    Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)CrossRefGoogle Scholar
  31. 31.
    Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans. Softw. Eng. 34(4), 485–496 (2008)CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.MCA DepartmentPESIT-BSCBangaloreIndia
  2. 2.MCA DepartmentAdhiyamaan College of EngineeringHosurIndia

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