Proceedings 12th International Symposium on Software Reliability Engineering
DOI: 10.1109/issre.2001.989454
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A Bayesian approach to reliability prediction and assessment of component based systems

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Cited by 92 publications
(90 citation statements)
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“…As a result, it is necessary to propagate the uncertainties in these parameters to the uncertainty in the estimate of the application reliability. A few research efforts that address this issue have limited applicability since they consider only a single type of architectural and component failure models [85], [38], [20]. In addition to uncertainty quantification, it is also necessary to establish a confidence interval for the reliability estimate to determine the risk associated with the estimate.…”
Section: Analysis Limitationsmentioning
confidence: 99%
“…As a result, it is necessary to propagate the uncertainties in these parameters to the uncertainty in the estimate of the application reliability. A few research efforts that address this issue have limited applicability since they consider only a single type of architectural and component failure models [85], [38], [20]. In addition to uncertainty quantification, it is also necessary to establish a confidence interval for the reliability estimate to determine the risk associated with the estimate.…”
Section: Analysis Limitationsmentioning
confidence: 99%
“…István et al [12,13] shed some light on ways to fully automate dependability analysis, applied to the Fault-Tolerant CORBA, using graph transformations into their VIATRA framework. The work from Singh et al [21] provides a prediction algorithm to analyse the reliability of the system prior to its construction. Their approach requires the user to provide global behavior scenarios other than the local behavior of the components interactions.…”
Section: Discussion and Related Workmentioning
confidence: 99%
“…service [i].sd [ j].s (15) Therefore, with hardware reliability considered case reliability is refined as…”
Section: Reliability Analysismentioning
confidence: 99%