2011): Real Reward Testing for Probabilistic Processes

We introduce a notion of real-valued reward testing for probabilistic processes by extending the tra-ditional nonnegative-reward testing with negative rewards. In this richer testing framework, the may-and must preorders turn out to be inverses. We show that for convergent processes with finitely many states and transitions, but not in the presence of divergence, the real-reward must-testing preorder coincides with the nonnegative-reward must-testing preorder. To prove this coincidence we charac-terise the usual resolution-based testing in terms of the weak transitions of processes, without having to involve policies, adversaries, schedulers, resolutions or similar structures that are external to the process under investigation. This requires establishing the continuity of our function for calculating testing outcomes.