Integrating Fault Removal Efficiency and S-Shaped Fault Detection in Software Reliability Growth Modeling

Software reliability stands as a paramount concern for developers and users alike. The efficiency of fault removal plays a vital aspect in achieving and maintaining this reliability. By assessing the percentage of resolved faults through the removal efficiency, developers can appraise the necessary effort, and users can gauge their confidence in the software. This paper introduces a software reliability growth model that incorporates fault removal efficiency within a nonhomogeneous Poisson process. Notably, an S-shaped function is employed to model the fault detection rate, enhancing the realism of the proposed model. The integration of this function adds nuance to the understanding of software reliability dynamics. To assess the model’s effectiveness, it is compared against existing models using software testing data.

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Published
2025-03-06
DOI
https://doi.org/10.1109/iatmsi64286.2025.10985351
Primary Topic
Software Reliability and Analysis Research
Type
article
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article

Integrating Fault Removal Efficiency and S-Shaped Fault Detection in Software Reliability Growth Modeling

Umashankar Samal, Masum Raj
Software Reliability and Analysis Research
article

Integrating Fault Removal Efficiency and S-Shaped Fault Detection in Software Reliability Growth Modeling

Umashankar Samal, Masum Raj
article en

Abstract

Software reliability stands as a paramount concern for developers and users alike. The efficiency of fault removal plays a vital aspect in achieving and maintaining this reliability. By assessing the percentage of resolved faults through the removal efficiency, developers can appraise the necessary effort, and users can gauge their confidence in the software. This paper introduces a software reliability growth model that incorporates fault removal efficiency within a nonhomogeneous Poisson process. Notably, an S-shaped function is employed to model the fault detection rate, enhancing the realism of the proposed model. The integration of this function adds nuance to the understanding of software reliability dynamics. To assess the model’s effectiveness, it is compared against existing models using software testing data.

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