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NON-HOMOGENEOUS POISSON PROCESS OF A QUADRATIC TRANSMUTED LOG-LOGISTIC MODEL
Keywords:
non-homogeneous Poisson process, software models, log-logistic distribution, quadratic transmuted distribution, maximum likelihood estimationDOI:
https://doi.org/10.17654/0972361724077Abstract
In recent years, software reliability engineering has become a major topic due to rapid technological advancements, as researchers work to interpret and analyze software reliability data. Therefore, establishing new software reliability models is essential to possess reliable tools for diagnosing software systems. This paper proposes a new NHPP model based on the quadratic transmuted log-logistic (QTLL) distribution. The characteristics of the model were obtained and graphically illustrated. Three real software data sets were applied to evaluate the proposed model based on different criteria. The parameters of the NHPP QTLL were estimated using maximum likelihood estimation (MLE), and nonlinear least squares estimation (NLSE), and a comparative analysis of the two estimation methods has been performed to estimate the model parameters. Additionally, a study was carried out to compare the suggested model with other well-known NHPP models. The results indicate that NLSE outperforms MLE in parameter estimation. Furthermore, the NHPP QTLL model demonstrated superior performance across all evaluation assessments compared to other models. The graphical representation also shows that the proposed model is more effective in fitting fault data than existing models.
Received: August 4, 2024
Revised: August 21, 2024
Accepted: September 26, 2024
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