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A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis

Author

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  • Kwang Yoon Song

    (Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855, USA)

  • In Hong Chang

    (Department of Computer Science and Statistics, Chosun University, 309 Pilmun-daero Dong-gu, Gwangju 61452, Korea)

  • Hoang Pham

    (Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Road, Piscataway, NJ 08855, USA)

Abstract

We have been attempting to evaluate software quality and improve its reliability. Therefore, research on a software reliability model was part of the effort. Currently, software is used in various fields and environments; hence, one must provide quantitative confidence standards when using software. Therefore, we consider the testing coverage and uncertainty or randomness of an operating environment. In this paper, we propose a new testing coverage model based on NHPP software reliability with the uncertainty of operating environments, and we provide a sensitivity analysis to study the impact of each parameter of the proposed model. We examine the goodness-of-fit of a new testing coverage model based on NHPP software reliability and other existing models based on two datasets. The comparative results for the goodness-of-fit show that the proposed model does significantly better than the existing models. In addition, the results for the sensitivity analysis show that the parameters of the proposed model affect the mean value function.

Suggested Citation

  • Kwang Yoon Song & In Hong Chang & Hoang Pham, 2019. "A Testing Coverage Model Based on NHPP Software Reliability Considering the Software Operating Environment and the Sensitivity Analysis," Mathematics, MDPI, vol. 7(5), pages 1-21, May.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:5:p:450-:d:232726
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    References listed on IDEAS

    as
    1. Qiuying Li & Hoang Pham, 2017. "A testing-coverage software reliability model considering fault removal efficiency and error generation," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-25, July.
    2. P. K. Kapur & H. Pham & A. Gupta & P. C. Jha, 2011. "Software Reliability Growth Models," Springer Series in Reliability Engineering, in: Software Reliability Assessment with OR Applications, chapter 0, pages 49-95, Springer.
    3. 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.
    4. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, March.
    5. Xuemei Zhang & Daniel R. Jeske & Hoang Pham, 2002. "Calibrating software reliability models when the test environment does not match the user environment," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 18(1), pages 87-99, January.
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    Cited by:

    1. Qing Tian & Chun-Wu Yeh & Chih-Chiang Fang, 2022. "Bayesian Decision Making of an Imperfect Debugging Software Reliability Growth Model with Consideration of Debuggers’ Learning and Negligence Factors," Mathematics, MDPI, vol. 10(10), pages 1-21, May.
    2. Hoang Pham, 2019. "A New Criterion for Model Selection," Mathematics, MDPI, vol. 7(12), pages 1-12, December.
    3. Tahere Yaghoobi & Man-Fai Leung, 2023. "Modeling Software Reliability with Learning and Fatigue," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
    4. Congcong Zhou & Zhenzhong Shen & Liqun Xu & Yiqing Sun & Wenbing Zhang & Hongwei Zhang & Jiayi Peng, 2023. "Global Sensitivity Analysis Method for Embankment Dam Slope Stability Considering Seepage–Stress Coupling under Changing Reservoir Water Levels," Mathematics, MDPI, vol. 11(13), pages 1-24, June.

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