Skip to main content
Log in

Nonparametric dynamically weighted combination model to determine when to stop testing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Software manufacturers need to minimize the number of their software failures in their production environments. So, software reliability becomes a critical factor for these manufacturers to focus on. Software Reliability Growth Models (SRGMs) are used as indicators of the number of failures that may be faced after the shipping of the software and thus are indicators of the readiness of the software for shipping. SRGMs to handle varying operational profiles have been proposed by researchers earlier. However, as it is difficult to predict the nature of the project in advance, the reliability engineer has to try out each model one at a time before zeroing in on the model to be used in the project. We have derived a combination model, called dynamically weighted infinite NHPP combination, using the existing models for determining the release time. The nonparametric dynamically weighted combination model that we propose was validated and was found to be effective.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Tariq I et al (2018) The comprehensive study on software reliability. In: IEEE International Conference on Computing, Mathematics and Engineering Technologies, iCoMET

  2. Musa JD (1999) Software reliability engineering. McGraw-Hill Publication, New York

    Google Scholar 

  3. Subburaj R (2015) Software reliability engineering. McGraw Hill Education (India)Private Limited, New Delhi, pp 61–80

    Google Scholar 

  4. Yamada S, Osaki S (1985) Software reliability growth modeling: models and applications. IEEE Trans Softw Eng SE-11(12):1431–1437

    Article  Google Scholar 

  5. Subburaj R, Gopal G, Kapur PK (2007) A software reliability growth model for vital quality metrics. S Afr J Ind Eng 18(2):93–108

    Google Scholar 

  6. Goel AL (1985) Software reliability models: assumptions, limitations and applicability. IEEE Trans Softw Eng SE-11(12):1411–1423

    Article  Google Scholar 

  7. Subburaj R, Gopal G, Kapur PK (2012) A software reliability growth model for estimating debugging and the learning indices. Int J Perform Eng 8(5):539–549

    Google Scholar 

  8. Farr W (1996) Software reliability modeling survey. McGraw-Hill, New York

    Google Scholar 

  9. Ramasamy S, Govindasamy G (2006) Generalized exponential Poisson model for software reliability growth. Int J Perform Eng 2(3):291–301

    Google Scholar 

  10. Ramasamy S, Govindasamy G (2006) A software reliability growth model addressing imperfect debugging and learning. In: Proceedings of International Conference on Reliability and Safety Engineering, pp 156–64

  11. Gupta A et al (2019) Analysis of software reliability models for reliability estimation. In: IEEE 9th International Conference on Cloud Computing, Data Science and Engineering

  12. Xiw M et al (2000) Software reliability models—past, present and future. In: Limmos, Nikulin M (eds.) Recent Advancement in Reliability Theory: Methodology Practise and Inference. Proceedings of International Conference on Mathematical Methods in Reliability (MMR 2000), France

  13. Farr W (1996) Design and code inspections to reduce errors in program development. IBM Syst J 15(3):182–211

    Google Scholar 

  14. Goel AL (1985) Software reliability models: assumptions, limitations and applicability. IEEE Trans Softw Eng SE 11(12):1411–1423

    Article  Google Scholar 

  15. The Guide to the Systems Engineering Body of Knowledge (SEBoK), v. 2.1, R.J. Cloutier (Editor in Chief) (2019). in SEBoK Editorial Board, Hoboken, NJ

  16. Vidhyashree Nagaraju (2018) Software reliability assessment: modeling and algorithms. IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), pp166–169. https://doi.org/10.1109/ISSREW.2018.000-4

  17. Ramasamy S, Preetha CASD (2016) Dynamically weighted combination model for describing inconsistent failure data of software projects. Indian J Sci Technol. https://doi.org/10.17485/ijst/2016/v9i35/90211

    Article  Google Scholar 

  18. Preetha CASD, Ramasamy S (2016) Importance of release time determination for success in software development projects. Int J Control Theory Appl 9(40):819–825

    Google Scholar 

  19. Rotella P, Chulani S (2017) Predicting release reliability. In: IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp 39–46

  20. Garg R, Gupta A (2018) Prediction of optimum release time of software ensuring high achievable reliability. In: IEEE International Conference on Advances in Computing, Communication Control and Networking Technologies

  21. Er-Qiang F, Jun Z (2017) A software reliability model based on failure mode. In: IEEE Fourth International Conference on Dependable Systems and Their Applications

  22. Zhu Mengmeng, Pham Hoang (2018) A two-phase software reliability modeling involving with software fault dependency and imperfect fault removal. Comput Lang Syst Struct Elsevier 53:27–42

    Google Scholar 

  23. Kapur PK, Garg RB (1990) Optimal software release policies for software reliability growth models under imperfect debugging. RAIRO 24:295–305

    Article  Google Scholar 

  24. Ramasamy Subburaj, Preetha CASD (2017) A new and repeatable methodology for estimating software stop testing time. Int J Pure Appl Math 116(23):145–155

    Google Scholar 

  25. Cao P, Yang K, Liu K (2019) Optimal selection and release problem in software testing process: a continuous time stochastic control approach. Elsevier Eur J Oper Res. https://doi.org/10.1016/j.ejor.2019.01.075

    Article  MATH  Google Scholar 

  26. Rani P, Mahapatra GS (2019) A novel approach of NPSO on dynamic weighted NHPP model for software reliability analysis with additional fault introduction parameter. Heliyon, Elsevier, Amsterdam

    Book  Google Scholar 

  27. Deiva Preetha CAS, Subburaj R (2018) Study on software reliability combination models. J Adv Res Dyn Control Syst 956–960

  28. Li Q, Pham H (2019) A generalized software reliability model with consideration of the uncertainty of operating environments. IEEE Access 7:84253–84567

    Article  Google Scholar 

  29. Musa JD, Ackerman AF (1989) Quantifying software validation: when to stop testing? IEEE Softw 6:19–27

    Article  Google Scholar 

  30. Huang C-Y, Lyu MR (2005) Optimal testing resource allocation, and sensitivity analysis in software development. IEEE Trans Reliab 54(4):592–603

    Article  Google Scholar 

  31. Kuo S-Y, Huang C-Y, Lyu MR (2001) Framework for modeling software reliability, using various testing-efforts and fault-detection rates. IEEE Trans Reliab 50(3):310–320

    Article  Google Scholar 

  32. Musa JD (1980) DACS Software reliability dataset, data and analysis center for software [Internet]. Accessed from: http://www.dacs.dtic.mil/databases/sled/swrel.shtml. Accessed 19 Sept 2005

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. A. S. Deiva Preetha.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deiva Preetha, C.A.S., Ramasamy, S. Nonparametric dynamically weighted combination model to determine when to stop testing. J Supercomput 76, 6065–6082 (2020). https://doi.org/10.1007/s11227-019-03125-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-03125-9

Keywords

Navigation