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Component based reliability prediction

Author

Listed:
  • 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))

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.

Suggested Citation

  • Sampa ChauPattnaik & Mitrabinda Ray & Mitali Madhusmita Nayak, 2021. "Component based reliability prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 391-406, June.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:3:d:10.1007_s13198-021-01079-x
    DOI: 10.1007/s13198-021-01079-x
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    References listed on IDEAS

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    1. Awad Ali & Dayang N. A. Jawawi & Mohd Adham Isa & Muhammad Imran Babar, 2016. "Technique for Early Reliability Prediction of Software Components Using Behaviour Models," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-24, September.
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    Cited by:

    1. Wassim R. Abou Ghaida & Ayman Baklizi, 2022. "Prediction of future failures in the log-logistic distribution based on hybrid censored data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1598-1606, August.

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