On real reward testing

We extend the traditional nonnegative reward testing with negative rewards. In this new testing framework, may preorder and must preorder are the inverse of each other. More surprisingly, it turns out that the real reward must testing is no more powerful than the nonnegative reward testing, at least for finite processes. In order to prove that result, we exploit an important property of failure simulation about the inclusion of the testing outcomes between two related processes.     

Finite axiomatization for symbolic probabilistic π-calculus

This paper focuses on the problem of seeking complete axiomatization for finite processes in the process calculus called symbolic probabilistic π-calculus introduced by Wu, Palamidessi and Lin. We provide inference systems for both strong and weak symbolic probabilistic bisimulations and also prove their soundness and completeness. This is the first work, to our knowledge, that provides complete axiomatization for symbolic probabilistic bisimulations in the presence of both nondeterministic and probabilistic choice.