Component based reliability prediction

Sampa ChauPattnaik (), Mitrabinda Ray () and Mitali Madhusmita Nayak ()
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Sampa ChauPattnaik: Sikha‘O’ Anusandhan (Deemed to be University)
Mitrabinda Ray: Sikha‘O’ Anusandhan (Deemed to be University)
Mitali Madhusmita Nayak: Sikha‘O’ Anusandhan (Deemed to be University)

International Journal of System Assurance Engineering and Management, 2021, vol. 12, issue 3, No 4, 406 pages

Abstract: Abstract Software components are measured in off the shelf at a great rate to develop reliable software applications. In component based systems, reliability estimation models are developed based on reliability of individual components and their transition probabilities. In this paper, we consider Scenario based Reliability Estimation using the hierarchical model (Discrete Time Markov Chain). In hierarchical model, variance of the number of visits to each component makes the system reliability closer to the estimated reliability value. We propose a reliability estimation model for the whole application. Component Dependency Graph of the systems, Reliability of individual components and transition probabilities between two components are considered as input for the estimation. Discrete Time Markov Chain is used to generate the mean and variance of the number of visits of the individual component from the initial component. This paper illustrates the reliability prediction and sensitivity analysis with examples.

Keywords: Discrete Time Markov Chain; Component dependency graph; State based models; Path based models; Sensitivity analysis (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-021-01079-x

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