This article presents a novel technique to calculate the probability that an adaptive system assumes a configuration. An important application area of dynamic adaptation is the cost-efficient development of dependable embedded systems. Dynamic adaptation exploits implicitly available redundancy, reducing the need for hardware redundancy, to make systems more available, reliable, survivable and, ultimately, more safe. Knowledge of configuration probabilities of a system is an essential requirement for the optimization of safety efforts in development. In perspective, it is also a prerequisite for dependability assessment.

Our approach is based on a modeling language for complex reconfiguration behavior. We transform the adaptation model into a probabilistic target model that combines a compositional fault tree with Markov chains. This hybrid model can be evaluated efficiently using a modified BDD-based algorithm. The approach is currently being implemented in an existing reliability modeling tool.