Characterisations of testing preorders for a finite probabilistic π -calculus

Author:

Deng Yuxin12,Tiu Alwen3

Affiliation:

1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

2. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

3. Research School of Computer Science, The Australian National University, Canberra, Australia

Abstract

Abstract We consider two characterisations of the may and must testing preorders for a probabilistic extension of the finite π -calculus: one based on notions of probabilistic weak simulations, and the other on a probabilistic extension of a fragment of Milner–Parrow–Walker modal logic for the π -calculus. We base our notions of simulations on similar concepts used in previous work for probabilistic CSP. However, unlike the case with CSP (or other non-value-passing calculi), there are several possible definitions of simulation for the probabilistic π -calculus, which arise from different ways of scoping the name quantification. We show that in order to capture the testing preorders, one needs to use the “earliest” simulation relation (in analogy to the notion of early (bi)simulation in the non-probabilistic case). The key ideas in both characterisations are the notion of a “characteristic formula” of a probabilistic process, and the notion of a “characteristic test” for a formula. As in an earlier work on testing equivalence for the π -calculus by Boreale and De Nicola, we extend the language of the π -calculus with a mismatch operator, without which the formulation of a characteristic test will not be possible.

Publisher

Association for Computing Machinery (ACM)

Subject

Theoretical Computer Science,Software

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Implementation relations and probabilistic schedulers in the distributed test architecture;Journal of Systems and Software;2017-10

2. Testing Finite Probabilistic Processes;Semantics of Probabilistic Processes;2014

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