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Modeling Software Reliability with Learning and Fatigue

Author

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  • Tahere Yaghoobi

    (Department of Computer Engineering and Information Technology, Payame Noor University, Tehran 19395-4697, Iran)

  • Man-Fai Leung

    (School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK)

Abstract

Software reliability growth models (SRGMs) based on the non-homogeneous Poisson process have played a significant role in predicting the number of remaining errors in software, enhancing software reliability. Software errors are commonly attributed to the mental errors of software developers, which necessitate timely detection and resolution. However, it has been observed that the human error-making mechanism is influenced by factors such as learning and fatigue. In this paper, we address the issue of integrating the fatigue factor of software testers into the learning process during debugging, leading to the development of more realistic SRGMs. The first model represents the software tester’s learning phenomenon using the tangent hyperbolic function, while the second model utilizes an exponential function. An exponential decay function models fatigue. We investigate the behavior of our proposed models by comparing them with similar SRGMs, including two corresponding models in which the fatigue factor is removed. Through analysis, we assess our models’ quality of fit, predictive power, and accuracy. The experimental results demonstrate that the model of tangent hyperbolic learning with fatigue outperforms the existing ones regarding fit, predictive power, or accuracy. By incorporating the fatigue factor, the models provide a more comprehensive and realistic depiction of software reliability.

Suggested Citation

  • Tahere Yaghoobi & Man-Fai Leung, 2023. "Modeling Software Reliability with Learning and Fatigue," Mathematics, MDPI, vol. 11(16), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3491-:d:1216239
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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. Vibha Verma & Sameer Anand & P. K. Kapur & Anu G. Aggarwal, 2022. "Unified framework to assess software reliability and determine optimal release time in presence of fault reduction factor, error generation and fault removal efficiency," 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. 13(5), pages 2429-2441, October.
    3. Wang, Jinyong & Wu, Zhibo, 2016. "Study of the nonlinear imperfect software debugging model," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 180-192.
    4. Hoang Pham, 2006. "System Software Reliability," Springer Series in Reliability Engineering, Springer, number 978-1-84628-295-9, March.
    5. 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.
    6. Chiu, Kuei-Chen & Huang, Yeu-Shiang & Lee, Tzai-Zang, 2008. "A study of software reliability growth from the perspective of learning effects," Reliability Engineering and System Safety, Elsevier, vol. 93(10), pages 1410-1421.
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