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Change point software belief reliability growth model considering epistemic uncertainties

Author

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  • Liu, Zhe
  • Wang, Shihai
  • Liu, Bin
  • Kang, Rui

Abstract

Various software reliability growth models (SRGMs) have been developed to evaluate software reliability. Since software faults are caused by bugs in the logic of the code, which are introduced due to human errors, software faults embody lots of epistemic uncertainties. Under the framework of uncertainty theory, a software belief reliability growth model (SBRGM) was proposed. Actually, during software testing fault detection rate function may show multiple phases due to changes of testing strategies and resources allocation. Thus, fault detection rate function may be discontinuous at some time known as the change point. Noting that the previous SBRGM did not consider the change point, this paper construct a new SBRGM incorporating change point, named CPSBRGM. Based on the new proposed model, reliability for software is evaluated under the framework of belief reliability theory. Method to estimate unknown parameters in the proposed model is also provided. The new proposed model is validated on three datasets and compared with the previous SBRGM. Results show that the new proposed model can clearly distinguish the change point during software testing, and is more in line with the actual software test situation than previous model without considering change points.

Suggested Citation

  • Liu, Zhe & Wang, Shihai & Liu, Bin & Kang, Rui, 2023. "Change point software belief reliability growth model considering epistemic uncertainties," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010809
    DOI: 10.1016/j.chaos.2023.114178
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    References listed on IDEAS

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    1. Boby John & Rajeshwar S. Kadadevaramath & Immanuel A. Edinbarough, 2019. "A fuzzy optimisation approach for software reliability estimation," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 13(2), pages 259-273.
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    4. Liu, Z. & Yang, Y., 2021. "Uncertain pharmacokinetic model based on uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    5. Liu, Z. & Yang, Y., 2021. "Pharmacokinetic model based on multifactor uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 392(C).
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