Abstract
Numerous Non-Homogenous Poisson Process based Software Reliability Growth Models have been developed in the past to assess reliability growth of the software system. In a contribution to existing literature, we study the impact of realistic factors encountered during the process of development. This paper proposes a reliability growth model incorporating Fault Reduction Factor, Fault Removal Efficiency and error generation to predict the reliability of the product released in multiple versions. Fault Reduction Factor has been modelled by Delayed S-shaped model, while Fault Removal Efficiency and Error Generation parameters are considered to be constant. Further, we validate the model on a four release real fault dataset of Tandem computers and employ data analysis techniques to plot goodness of fit curves and boxplots to investigate predictive accuracy of proposed model. We have also judged the experimental outcomes considering three special cases. It is witnessed that the proposed model well captures the failure phenomenon and the parameters considered significantly impacts the accuracy.
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Verma, V., Anand, S., Aggarwal, A.G. (2020). Reliability Assessment of Multi-release Software System Under Imperfect Fault Removal Phenomenon. In: Kapur, P.K., Singh, G., Klochkov, Y.S., Kumar, U. (eds) Decision Analytics Applications in Industry. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-3643-4_29
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DOI: https://doi.org/10.1007/978-981-15-3643-4_29
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