Performance analysis of stochastic process algebra models using stochastic simulation

We present a translation of a generic stochastic process algebra model into a form suitable for stochastic simulation. By systematically generating rate equations from a process description, we can use tools developed for chemical and biochemical reaction analysis to provide time-series output for models with state spaces of O(10¹⁰⁰⁰⁰) and beyond. We apply these techniques to a significant case study: that of a secure electronic voting protocol