Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Development of software reliability models using a hybrid approach and validation of the proposed models using big data

  • 159 Accesses

Abstract

This article proposes three software reliability models based on a hybrid approach combining NHPP models, Weibull model, and exponential model. The software failure is first categorised into three categories, namely pure software failures, hardware-induced software failures, and user-induced software failures. Based on the failure behaviour, NHPP models were adapted for pure software failures, Weibull model for hardware-induced failures, and exponential model for user-induced software failures. The failure intensity function, mean value function, and reliability function were determined. The proposed models are validated using big data analysis. From the data collected during the testing phase, the optimal values of parameters were estimated using maximum likelihood estimation and genetic algorithm. The expected number of failures and the cumulative number of failures were calculated, and comparison was made between the observed values to show the performance of the proposed models. A comparison criterion was also proposed to confirm the estimation accuracy. Finally, a t test was conducted to test the significance of the difference between the observed and estimated values. Experimental results confirm the better estimation accuracy of the proposed models.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. 1.

    Smith DJ (2011) Reliability, maintainability and risk. Reliab Maintainab Risk. https://doi.org/10.1016/B978-0-08-096902-2.00003-9

  2. 2.

    Sommerville I (2010) Software engineering. Softw Eng. https://doi.org/10.1111/j.1365-2362.2005.01463.x

  3. 3.

    Yamada S, Ohtera H, Ohba M (1992) Testing-domain dependent software reliability models. Comput Math Appl 24(1–2):79–86. https://doi.org/10.1016/0898-1221(92)90231-6

  4. 4.

    Goel AL (1985) Software reliability models: assumptions, limitations, and applicability. IEEE Trans Softw Eng SE-11(12):1411–1423. https://doi.org/10.1109/TSE.1985.232177

  5. 5.

    Schick GJ, Wolverton RW (1978) An analysis of competing software reliability models. IEEE Trans Softw Eng SE-4(2):104–120. https://doi.org/10.1109/TSE.1978.231481

  6. 6.

    Iqbal R, Doctor F, More B, Mahmud S, Yousuf U (2016) Big data analytics: computational intelligence techniques and application areas. Int J Inf Manag. https://doi.org/10.1016/j.ijinfomgt.2016.05.020

  7. 7.

    Goel AL, Okumoto K (1979) Time-dependent error-detection rate model for software reliability and other performance measures. IEEE Trans Reliab R-28(3):206–211. https://doi.org/10.1109/TR.1979.5220566

  8. 8.

    Yamada S, Hishitani J, Osaki S (1993) Software-reliability growth with a Weibull test-effort: a model and application. IEEE Trans Reliab 42(1):100–106. https://doi.org/10.1109/24.210278

  9. 9.

    Miller DR (1986) Exponential Order Statistic Models of Software Reliability Growth. IEEE Trans Softw Eng SE-12(1):12–24. https://doi.org/10.1109/TSE.1986.6312915

  10. 10.

    Yamada S, Ohba M, Osaki S (1984) S-shaped software reliability growth models and their applications. IEEE Trans Reliab R-33(4):289–292. https://doi.org/10.1109/TR.1984.5221826

  11. 11.

    Kapur PK, Garg RB (1991) Optimum release policy for an inflection s-shaped software reliability growth model. Microelectron Reliab 31(1):39–41. https://doi.org/10.1016/0026-2714(91)90343-6

  12. 12.

    Hossain SA, Dahiya RC (1993) Estimating the parameters of a non-homogeneous poisson-process model for software reliability. IEEE Trans Reliab 42(4):604–612. https://doi.org/10.1109/24.273589

  13. 13.

    Yadav HB, Yadav DK (2017) Early software reliability analysis using reliability relevant software metrics. Int J Syst Assur Eng Manag 8(S4):2097–2108. https://doi.org/10.1007/s13198-014-0325-3

  14. 14.

    Lakshmanan I, Ramasamy S (2015) An artificial neural-network approach to software reliability growth modeling. Procedia Comput Sci 57:695–702. https://doi.org/10.1016/j.procs.2015.07.450

  15. 15.

    Minohara T, Tohma Y (1995) Parameter estimation of hyper-geometric distribution software reliability growth model by genetic algorithms. In: Proceedings of the Sixth International Symposium on Software Reliability Engineering. https://doi.org/10.1109/ISSRE.1995.497673

  16. 16.

    Choudhary A, Baghel AS, Sangwan OP (2018) Parameter estimation of software reliability model using firefly optimization. Adv Intell Syst Comput 542:407–415. https://doi.org/10.1007/978-981-10-3223-3_39

  17. 17.

    Majumdar R, Kapur PK, Khatri SK, Shrivastava AK (2018) Evolutionary algorithm based faults optimization of multi-modular software. Smart Innov Syst Technol 78:281–291. https://doi.org/10.1007/978-981-10-5547-8_30

  18. 18.

    Otero CE, Peter A (2015) Research directions for engineering big data analytics software. IEEE Intell Syst 30(1):13–19. https://doi.org/10.1109/MIS.2014.76

  19. 19.

    Meeker WQ, Hong Y (2014) Reliability meets big data: opportunities and challenges. Qual Eng 26:102–116. https://doi.org/10.1080/08982112.2014.846119

  20. 20.

    Sanborn AN (2017) Types of approximation for probabilistic cognition: sampling and variational. Brain Cogn 112:98–101. https://doi.org/10.1016/j.bandc.2015.06.008

  21. 21.

    Sanborn AN (2017) Types of approximation for probabilistic cognition: sampling and variational. Brain Cogn 112:98–101. https://doi.org/10.1016/j.bandc.2015.06.008

  22. 22.

    Sharma K, Garg R, Nagpal CK, Garg RK (2010) Selection of optimal software reliability growth models using a distance based approach. IEEE Trans Reliab 59(2):266–276. https://doi.org/10.1109/TR.2010.2048657

  23. 23.

    Lo J-H, Huang C-Y, Chen I-Y, Kuo S-Y, Lyu MR (2005) Reliability assessment and sensitivity analysis of software reliability growth modeling based on software module structure. J Syst Softw 76(1):3–13. https://doi.org/10.1016/j.jss.2004.06.025

  24. 24.

    Wang J, Zhang C (2018) Software reliability prediction using a deep learning model based on the RNN encoder–decoder. Reliab Eng Syst Saf 170:73–82. https://doi.org/10.1016/j.ress.2017.10.019

  25. 25.

    Montgomery DC (2012) Design and analysis of experiments. Wiley, New York. https://doi.org/10.1198/tech.2006.s372

  26. 26.

    Park J, Kim HJ, Shin JH, Baik J (2012) An embedded software reliability model with consideration of hardware related software failures. In Proceedings of the 2012 IEEE 6th International Conference on Software Security and Reliability, SERE 2012, pp 207–214. https://doi.org/10.1109/SERE.2012.10

Download references

Author information

Correspondence to P. Govindasamy.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Govindasamy, P., Dillibabu, R. Development of software reliability models using a hybrid approach and validation of the proposed models using big data. J Supercomput (2018). https://doi.org/10.1007/s11227-018-2457-8

Download citation

Keywords

  • Software failures
  • Estimation accuracy
  • Parameters
  • Reliability models
  • Hardware
  • Big data analysis