Abstract
In this study, we present an approach to enhance software reliability, acknowledging the evolving understanding of error dynamics within software development. While traditional models predominantly attribute errors to coding mistakes, recent insights emphasize the role of human factors such as learning processes and fatigue. Our method integrates these insights by incorporating the fatigue factor of software testers and optimizing fault removal efficiency within the debugging process. This integration leads to the formulation of more realistic software reliability growth models, characterized by S-shaped learning curves and an exponential fatigue function. We conduct a thorough analysis of the models’ quality, predictive abilities, and accuracy, evaluating them against three established fit criteria. By encompassing learning, fatigue, and fault removal efficiency within our models, we provide a comprehensive framework for understanding the dynamics of software reliability.
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Umashankar Samal conducted all the research activities and calculations for this study and took primary responsibility for drafting the manuscript and incorporating the research findings into a coherent narrative. Ajay Kumar provided valuable guidance and oversight throughout the research process. This included reviewing and proofreading the manuscript for clarity, accuracy, and scientific rigor.
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Samal, U., Kumar, A. Incorporating human dynamics into software reliability analysis: learning, fatigue, and efficiency considerations. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02368-x
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DOI: https://doi.org/10.1007/s13198-024-02368-x