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http://dx.doi.org/10.6109/jicce.2022.20.2.79

Component-Based System Reliability using MCMC Simulation  

ChauPattnaik, Sampa (Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University)
Ray, Mitrabinda (Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University)
Nayak, Mitalimadhusmita (Department of Mathematics, Siksha 'O' Anusandhan (Deemed to be) University)
Patnaik, Srikanta (Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be) University)
Publication Information
Journal of information and communication convergence engineering / v.20, no.2, 2022 , pp. 79-89 More about this Journal
Abstract
To compute the mean and variance of component-based reliability software, we focused on path-based reliability analysis. System reliability depends on the transition probabilities of components within a system and reliability of the individual components as basic input parameters. The uncertainty in these parameters is estimated from the test data of the corresponding components and arises from the software architecture, failure behaviors, software growth models etc. Typically, researchers perform Monte Carlo simulations to study uncertainty. Thus, we considered a Markov chain Monte Carlo (MCMC) simulation to calculate uncertainty, as it generates random samples through sequential methods. The MCMC approach determines the input parameters from the probability distribution, and then calculates the average approximate expectations for a reliability estimation. The comparison of different techniques for uncertainty analysis helps in selecting the most suitable technique based on data requirements and reliability measures related to the number of components.
Keywords
Component Based Systems; MCMC; System Reliability; Discrete Time Markov Chain;
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