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A generalized multiple environmental factors software reliability model with stochastic fault detection process

Author

Listed:
  • Mengmeng Zhu

    (North Carolina State University)

  • Hoang Pham

    (Rutgers University)

Abstract

Software systems have been widely applied in numerous safety–critical domains; however, large-scale software development is still considered as a complicated and expensive activity. As the latest trends in software industry accelerate the complexity and dependency of software development, such complicated and human-centered process needs to be addressed well. Meanwhile, recent survey investigations (Zhu et al. in J Syst Softw 109:150–160, 2015; Zhu and Pham in J Syst Softw 132:72–84, 2017) revealed that environmental factors, defined from software development, have significant impacts on software reliability. Considering such significant impacts, we first propose a generalized multiple-environmental-factors software reliability growth model with multiple environmental factors and the associated randomness under the martingale framework. The randomness is reflected on the process of detecting software fault. Indeed, this is a stochastic fault detection process. As an illustration, a specific multiple-environmental-factors software reliability growth model incorporating two specific environmental factors, percentage of reused modules and frequency of program specification change, is further developed. Lastly, we employ two real-world data sets to demonstrate the prediction performance of the proposed generalized multiple-environmental-factors software reliability growth model.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:311:y:2022:i:1:d:10.1007_s10479-020-03732-3
    DOI: 10.1007/s10479-020-03732-3
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    References listed on IDEAS

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    1. 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.
    2. Rahul Basole & Jürgen Karla, 2011. "On the Evolution of Mobile Platform Ecosystem Structure and Strategy," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 3(5), pages 313-322, October.
    3. Linet Özdamar & Ebru Alanya, 2001. "Uncertainty Modelling in Software Development Projects (With Case Study)," Annals of Operations Research, Springer, vol. 102(1), pages 157-178, February.
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    5. 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.
    6. Babu Zachariah, 2015. "Optimal stopping time in software testing based on failure size approach," Annals of Operations Research, Springer, vol. 235(1), pages 771-784, December.
    7. Mengmeng Zhu & Hoang Pham, 2018. "A multi-release software reliability modeling for open source software incorporating dependent fault detection process," Annals of Operations Research, Springer, vol. 269(1), pages 773-790, October.
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    Cited by:

    1. 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.

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