Simulations often depend heavily on random numbers, yet the impact of random number generators is recognized seldom. The generation of random numbers for simulations is not trivial, as the quality of each algorithm depends on the simulation scenario. Therefore, simulation environments for large-scale experimentation with safety-critical models require a reliable mechanism to cope with this aspect. We show how to address this problem by realizing a random number generation architecture for a

more »

... ral-purpose simulation system. It provides various random number generators (RNGs), probability distributions, and RNG tests. It is open to future additions, which allows the assessment of new generators in a simulation context and the re-validation of past simulation studies. We present a short example that illustrates why the features of such an architecture are essential for getting valid results.