Applications of Spiking Neural Network to Predict Software Reliability

  • Ramakanta Mohanty
  • Aishwarya Priyadarshini
  • Vishnu Sai Desai
  • G. Sirisha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)

Abstract

In the period of software improvement, programming dependability expectation turned out to be exceptionally critical for creating nature of programming in the product business. Time to time, numerous product dependability models have been introduced for evaluating unwavering quality of programming in programming forecast models. However, building precise forecast model is hard because of intermittent changes in information in the space of programming designing. As needs be, we propose a novel procedure, i.e. spiking neural system to anticipate programming unwavering quality. The key goal of this paper is to exhibit another approach which upgrades the exactness of programming unwavering quality prescient models when utilized with the product disappointment dataset. The viability of quality of a product is exhibited on dataset taken from the literature, where execution is measured by utilizing normalized root mean square error (NRMSE) obtained in the test dataset.

Keywords

Software reliability Neural network Spiking neural network Normalized root mean square error 

References

  1. 1.
    Musa, J.D.: Software Reliability Engineering: More Reliable Software Faster and Cheaper, 2nd edn. Tata McGraw-Hill EditionGoogle Scholar
  2. 2.
    Lyu, M.R.: Handbook of Software Reliability Engineering. IEEE Computer Society Press and McGraw Hill (ed.) (1996)Google Scholar
  3. 3.
    Khoshgoftaar, T.M., Allen, E.B., Jones, W.D., Hudepohi, J.P.: Classification—Tree models of software quality over multiple releases. IEEE Trans. Reliab. 49(1), 4–11 (2000)CrossRefGoogle Scholar
  4. 4.
    Khohgoaftaar, T.M., Allen, E.B., Hudepohl, J.P., Aid, S.J.: Applications of neural networks to software quality modelling of a very large telecommunications system. IEEE Trans. Neural Netw. 8(4), 902–909 (1997)CrossRefGoogle Scholar
  5. 5.
    Cai, K., Yuan, C., Zhang, M.L.: A critical review on software reliability modelling. Reliab. Eng. Syst. Saf. 32, 357–371 (1991)CrossRefGoogle Scholar
  6. 6.
    Cai, K.Y., Cai, L., Wang, W.D., Yu, Z.Y., Zhang, D.: On the neural network approach in software reliability modelling. J. Syst. Softw. 58, 47–62 (2001)CrossRefGoogle Scholar
  7. 7.
    Tian, L., Noore, A.: Evolutionary neural network modelling for software cumulative failure time prediction. Reliab. Eng. Syst. Saf. 87, 45–51 (2005)CrossRefGoogle Scholar
  8. 8.
    Su, A., Chin, Y., Huang, U.: Neural-network based approaches for software reliability estimation using dynamic weighted combinational models. J. Syst. Softw. 80, 606–615 (2007)CrossRefGoogle Scholar
  9. 9.
    Ho, S.L., Xie, M., Goh, T.N.: A study of the connectionist models for software reliability predictions. Comput. Math Appl. 46, 1037–1045 (2003)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Su, Y.S., Huang, C.Y.: Neural-network based approaches for software reliability estimation using dynamic weighted combinational models. J. Syst. Softw. (2006).  https://doi.org/10.1016/j.jss.2006.06.017
  11. 11.
    Huang, C.Y., Lyle, M.R., Kuo, S.Y.: A unified scheme of some non-homogenous poisons process model for software reliability estimation. IEEE Trans. Softw. Eng. 29(3), 261–269 (2003)CrossRefGoogle Scholar
  12. 12.
    Costa, E.O.,Vergili, O.S.R., Souz, G.: Modeling software reliability growth with genetic algorithm. In: Proceedings of 16th IEEE International Symposium on Software Reliability Engineering, pp. 170–180 (2005)Google Scholar
  13. 13.
    Pai, P.F., Hong, W.C.: Software reliability forecasting by support vector machine with simulated annealing algorithms. J. Syst. Softw. 79(6), 747–755 (2006).  https://doi.org/10.1016/j.jss.2005.02.025CrossRefGoogle Scholar
  14. 14.
    Rajkiran, N., Ravi, V.: Software Reliability prediction by soft computing technique. J. Syst. Softw. 81(4), 576–583 (2007).  https://doi.org/10.1016/j.jss.2007.05.005CrossRefGoogle Scholar
  15. 15.
    Ravi, V., Chauhan, N.J., Rajkiran, N.: Software reliability prediction using intelligent techniques: application to operational risk prediction in Firms. Int. J. Comput. Intell. Appl. 8(2), 181–194 (2009).  https://doi.org/10.1142/S1469026809002588CrossRefMATHGoogle Scholar
  16. 16.
    Mohanty R.K., Ravi, V.: Machine learning techniques to predict software defects. In: Encyclopedia of Business Analytics and Optimization, vol. 5, pp. 1422–1434. Elsevier (2014)Google Scholar
  17. 17.
    Mohanty R.K., Ravi, V., Patra, M.R.: Hybrid intelligent systems for predicting software reliability. Appl. Soft Comput. 13, 180–200 (2013) (Elsevier)CrossRefGoogle Scholar
  18. 18.
    Mohanty, R.K., Ravi, V., Patra, M.R.: Machine learning and intelligent technique to predict software reliability. Int. J. Appl. Evol. Comput. 1(3), 70–86 (2010)CrossRefGoogle Scholar
  19. 19.
    Mohanty R.K., Ravi, V., Patra, M.R.: Software Reliability prediction using genetic programming. In: The IEEE International Conferences of the Biologically Inspired Computing and Applications (BICA-2009), Bhubaneswar, India, pp. 331–336 (2009)Google Scholar
  20. 20.
    Mohanty R.K., Ravi, V., Patra, M.R.: Software reliability prediction using group method of data handling. In: International Conferences on RSFDGrC’ 2009’, LNAI 5908, IIT-New Delhi, pp. 344–351. Springer (2009)Google Scholar
  21. 21.
    Mohanty R.K., Naik, V., Mubeen, A.: Application of ant colony optimization techniques to predict software reliability. In: IEEE International Conference on Communication Systems and Network Technologies (CSNT-2014), Bhopal, India, pp. 494–500, IEEE (2014)Google Scholar
  22. 22.
    Gautam, A., Bhateja V., Tiwary, A., Sathpathy S.C.: An Improved Memmogram Classification Approach Using Back propagation Neural network, Data Engineering and Intelligent Computing, pp. 369–376. Springer, Singapore (2018)Google Scholar
  23. 23.
    NatschlNager, T., Ruf, B.: Spatial and temporal pattern analysis via spiking neurons. Netw. Comput. Neural Syst. 9(3), 319–332 (1998)CrossRefGoogle Scholar
  24. 24.
    Meftah, B., Le’zoray, O., Chaturvedi, S., Khurshid, A., Benyettou, A.: Image Processing with Spiking Neuron Networks, Artificial Intelligence, Evolutionary Computing and Metaheuristic, SCI 427, pp. 525–544. Springer, Berlin (2013)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Ramakanta Mohanty
    • 1
  • Aishwarya Priyadarshini
    • 2
  • Vishnu Sai Desai
    • 1
  • G. Sirisha
    • 1
  1. 1.Department of Computer Science & EngineeringKeshav Memorial Institute of TechnologyHyderabadIndia
  2. 2.Department of Computer Science and EngineeringInternational Institute of Information TechnologyBhubaneswarIndia

Personalised recommendations