Annals of Software Engineering
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Combinatorial models such as fault trees and reliability block diagrams are efficient for model specification and often efficient in the evaluation of system models. But it is difficult, if not impossible, to allow for various types of dependencies (such as repair dependency and near-coincident-fault type dependency), transient and intermittent faults, standby systems with warm spares, and so forth. Markov models can capture such important system behavior, but the size of a Markov model may grow exponentially with the number of components in the system. This paper presents an approach for avoiding the large state space problem. The approach uses a hierarchical modeling technique for analyzing complex reliability models. It allows the flexibility of Markov models where necessary and retains the efficiency of combinatorial solution where possible. Based on this approach a computer program called SHARPE (Symbolic Hierarchical Automated Reliability and Performance Evaluator) has been written. Examples are presented that show how combinations of models can be used to evaluate the reliability and availability of large systems using SHARPE.