IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v176y2023ics0960077923010809.html
   My bibliography  Save this article

Change point software belief reliability growth model considering epistemic uncertainties

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923010809
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2023.114178?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tingqing Ye & Baoding Liu, 2023. "Uncertain hypothesis test for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(2), pages 195-211, June.
    2. Tamura, Yoshinobu & Yamada, Shigeru, 2006. "A flexible stochastic differential equation model in distributed development environment," European Journal of Operational Research, Elsevier, vol. 168(1), pages 143-152, January.
    3. Liu, Z. & Yang, Y., 2021. "Uncertain pharmacokinetic model based on uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    4. 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.
    5. Liu, Z. & Yang, Y., 2021. "Pharmacokinetic model based on multifactor uncertain differential equation," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Caiwen Gao & Zhiqiang Zhang & Baoliang Liu, 2022. "Uncertain Population Model with Jumps," Mathematics, MDPI, vol. 10(13), pages 1-12, June.
    2. Tingqing Ye & Baoding Liu, 2023. "Uncertain hypothesis test for uncertain differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 22(2), pages 195-211, June.
    3. Liu, Zhe & Li, Xiaoyang & Kang, Rui, 2022. "Uncertain differential equation based accelerated degradation modeling," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Shen, Jiayu & Shi, Jianxin & Gao, Lingceng & Zhang, Qiang & Zhu, Kai, 2023. "Uncertain green product supply chain with government intervention," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 136-156.
    5. Chen, Dan & Liu, Yang, 2023. "Uncertain Gordon-Schaefer model driven by Liu process," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    6. Liu, Z. & Yang, Y., 2021. "Selection of uncertain differential equations using cross validation," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    7. Jia, Lifen & Liu, Xueyong, 2021. "Optimal harvesting strategy based on uncertain logistic population model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    8. Meiling Jin & Fengming Liu & Yufu Ning & Yichang Gao & Dongmei Li, 2024. "A Mathematical Optimization Model Designed to Determine the Optimal Timing of Online Rumor Intervention Based on Uncertainty Theory," Mathematics, MDPI, vol. 12(16), pages 1-21, August.
    9. Zhiwei Wang & Yuanguo Zhu, 2024. "LQ optimal control of uncertain fractional differential systems," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 577-597, December.
    10. 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.
    11. Yang Liu & Zhongfeng Qin & Xiang Li, 2024. "Are the queueing systems in practice random or uncertain? Evidence from online car-hailing data in Beijing," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 497-511, December.
    12. Udom, Akaninyene Udo, 2012. "Exponential stabilization of stochastic interval system with time dependent parameters," European Journal of Operational Research, Elsevier, vol. 222(3), pages 523-528.
    13. Lifen Jia & Linya Zhang & Wei Chen, 2024. "China’s carbon emission allowance prices forecasting and option designing in uncertain environment," Fuzzy Optimization and Decision Making, Springer, vol. 23(4), pages 539-560, December.
    14. Jinran Chen, 2024. "Prediction of global trade network evolution with uncertain multi-step time series forecasting method," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 387-414, September.
    15. Zhe Liu & Yanbin Li, 2024. "Pricing and valuation of carbon swap in uncertain finance market," Fuzzy Optimization and Decision Making, Springer, vol. 23(3), pages 319-336, September.
    16. He, Liu & Zhu, Yuanguo, 2024. "Nonparametric estimation for uncertain fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    17. Chih-Chiang Fang & Chun-Wu Yeh, 2016. "Effective confidence interval estimation of fault-detection process of software reliability growth models," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(12), pages 2878-2892, September.
    18. Shakshi Singhal & P. K. Kapur & Vivek Kumar & Saurabh Panwar, 2024. "Stochastic debugging based reliability growth models for Open Source Software project," Annals of Operations Research, Springer, vol. 340(1), pages 531-569, September.
    19. Singhal, Shakshi & Anand, Adarsh & Singh, Ompal, 2020. "Studying dynamic market size-based adoption modeling & product diffusion under stochastic environment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    20. Qian, Yanjun & Xie, Min & Goh, Thong Ngee & Lin, Jun, 2010. "Optimal testing strategies in overlapped design process," European Journal of Operational Research, Elsevier, vol. 206(1), pages 131-143, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010809. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.