We discuss the role of modeling in the design and validation of life-critical, real-time systems. The basics of Markov, Markov reward, and stochastic reward net models are covered. An example of a nuclear power plant cooling system is developed in detail. Multilevel models, model calibration, and model validation are also discussed.>

The authors examine the problem of accurately modeling the software fault-tolerance technique based on recovery blocks. Analysis of some systems have investigated the correlation between software modules, which may be due to a portion of the functional specification that is common to all software modules, or to the inherent hardness of some problems. Three types of dependence which can be captured using measurements are considered. These are correlation between software modules for a single input, correlation between successive acceptance tests on correct module outputs and incorrect module outputs, and correlation between subsequent inputs. The authors' technique is quite general and can be applied to other types of correlation. In accounting for dependence, they use the intensity distribution introduced by D.E. Eckhardt and L.D. Lee (1985). A method of generating the intensity distribution that is based on the pairwise correlation between modules is discussed. This method is contrasted with the assumption of independent modules as well as the use of the beta-binomial density introduced by V.F. Nicola and A. Goyai (1990). The effects of dependencies were studied using a Stochastic Reward Network (SRN) that incorporates all of the above dependencies and a modeling tool called Stochastic Petri Net Package (SPNP).>