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A generalized software reliability model with stochastic fault-detection rate

Author

Listed:
  • Triet Pham

    (Rutgers University)

  • Hoang Pham

    (Rutgers University)

Abstract

We propose a theoretic model of software reliability where the fault detection rate is a stochastic process. This formulation provides the flexibility in modeling the random environment effects in testing software data. We examine two particular cases: additive and multiplicative noise and provide explicit representations for the expected number of software failures. Examples are included to demonstrate the formulas for specific choices of time dependent total number of faults and distribution of noise.

Suggested Citation

  • Triet Pham & Hoang Pham, 2019. "A generalized software reliability model with stochastic fault-detection rate," Annals of Operations Research, Springer, vol. 277(1), pages 83-93, June.
    • Handle: RePEc:spr:annopr:v:277:y:2019:i:1:d:10.1007_s10479-017-2486-3
    DOI: 10.1007/s10479-017-2486-3
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    References listed on IDEAS

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    1. Yuka Minamino & Shinji Inoue & Shigeru Yamada, 2016. "NHPP-based change-point modeling for software reliability assessment and its application to software development management," Annals of Operations Research, Springer, vol. 244(1), pages 85-101, September.
    2. Pham, Hoang & Zhang, Xuemei, 2003. "NHPP software reliability and cost models with testing coverage," European Journal of Operational Research, Elsevier, vol. 145(2), pages 443-454, March.
    3. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, September.
    4. Editors, 2014. "International Journal of Systems Science," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(12), pages 1-1, December.
    5. Hiroyuki Okamura & Tadashi Dohi, 2016. "Phase-type software reliability model: parameter estimation algorithms with grouped data," Annals of Operations Research, Springer, vol. 244(1), pages 177-208, September.
    6. Shinji Inoue & Jun Ikeda & Shigeru Yamada, 2016. "Bivariate change-point modeling for software reliability assessment with uncertainty of testing-environment factor," Annals of Operations Research, Springer, vol. 244(1), pages 209-220, September.
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    Citations

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    Cited by:

    1. Vibha Verma & Abhishek Tandon & Anu G. Aggarwal, 2022. "The Moderating Effect of Management Review in Enhancing Software Reliability: A Partial Least Square Approach," Information Systems Frontiers, Springer, vol. 24(6), pages 1845-1863, December.
    2. Da Hye Lee & In Hong Chang & Hoang Pham, 2020. "Software Reliability Model with Dependent Failures and SPRT," Mathematics, MDPI, vol. 8(8), pages 1-14, August.
    3. Ritu Bibyan & Sameer Anand & Anu G. Aggarwal & Abhishek Tandon, 2023. "Multi-release testing coverage-based SRGM considering error generation and change-point incorporating the random effect," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(5), pages 1877-1887, October.
    4. Hoang Pham, 2020. "On Estimating the Number of Deaths Related to Covid-19," Mathematics, MDPI, vol. 8(5), pages 1-9, April.
    5. Mengmeng Zhu & Hoang Pham, 2022. "A generalized multiple environmental factors software reliability model with stochastic fault detection process," Annals of Operations Research, Springer, vol. 311(1), pages 525-546, April.
    6. Sangeeta & Kapil Sharma & Manju Bala, 2020. "An ecological space based hybrid swarm-evolutionary algorithm for software reliability model parameter estimation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(1), pages 77-92, February.

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