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Multi-release software model based on testing coverage incorporating random effect (SDE)

In the past, various Software Reliability Growth Models (SRGMs) have been proposed using different parameters to improve software worthiness. Testing Coverage is one such parameter that has been studied in numerous models of software in the past and it has proved its influence on the reliability mod...

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Detalles Bibliográficos
Autores principales: Bibyan, Ritu, Anand, Sameer, Aggarwal, Anu G., Kaur, Gurjeet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9971062/
https://www.ncbi.nlm.nih.gov/pubmed/36865647
http://dx.doi.org/10.1016/j.mex.2023.102076
Descripción
Sumario:In the past, various Software Reliability Growth Models (SRGMs) have been proposed using different parameters to improve software worthiness. Testing Coverage is one such parameter that has been studied in numerous models of software in the past and it has proved its influence on the reliability models. To sustain themselves in the market, software firms keep upgrading their software with new features or enhancements by rectifying previously reported faults. Also, there is an impact of the random effect on testing coverage during both the testing and operational phase. In this paper, we have proposed a Software reliability growth model based on testing coverage with random effect along with imperfect debugging. Later, the multi-release problem is presented for the proposed model. The proposed model is validated on the dataset from Tandem Computers. The results for each release of the models have been discussed based on the different performance criteria. The numerical results illustrate that models fit the failure data significantly. • The random effect in the testing coverage rate is handled using Stochastic Differential Equations (SDE). • Three testing coverage functions used are Exponential, Weibull, and S-shaped. • Four Releases of the software model has been presented.