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<p>Enhancing software quality after the development phase is recognized as a challenging task. Software project managers face several significant challenges, including elevated failure rates and delayed delivery costs, in their effort to produce robust and reliable software systems. They must efficiently manage and allocate resources such as budget, time, workforce, and testing effort to ensure timely and cost-effective delivery. Numerous software reliability models based on testing effort have been discussed in the existing literature, typically assuming a static development environment. However, real-world development settings often vary due to multiple factors, and such changes should be accounted for when evaluating the predictive capability of these models. Addressing these limitations, this study presents a stochastic software reliability growth model (SRGM) that incorporates testing effort and a nonlinear fault introduction rate. The effectiveness of the proposed model is investigated using two real-world software failure datasets to assess its applicability and effectiveness. The results are then assessed and contrasted with existing models, demonstrating that the proposed SRGM achieves superior accuracy and predictive performance.</p>

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Software fault prediction modelling with effort and dynamic fault introduction rate: a stochastic approach

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Avinash K Shrivastava,
  • P. K. Kapur,
  • Vivek Kumar

摘要

Enhancing software quality after the development phase is recognized as a challenging task. Software project managers face several significant challenges, including elevated failure rates and delayed delivery costs, in their effort to produce robust and reliable software systems. They must efficiently manage and allocate resources such as budget, time, workforce, and testing effort to ensure timely and cost-effective delivery. Numerous software reliability models based on testing effort have been discussed in the existing literature, typically assuming a static development environment. However, real-world development settings often vary due to multiple factors, and such changes should be accounted for when evaluating the predictive capability of these models. Addressing these limitations, this study presents a stochastic software reliability growth model (SRGM) that incorporates testing effort and a nonlinear fault introduction rate. The effectiveness of the proposed model is investigated using two real-world software failure datasets to assess its applicability and effectiveness. The results are then assessed and contrasted with existing models, demonstrating that the proposed SRGM achieves superior accuracy and predictive performance.