Generalized Parameter Lifting: Finer Abstractions for Parametric Markov Chains

arXiv (Cornell University)

Abstract

Parametric Markov chains (pMCs) are Markov chains (MCs) with symbolic probabilities. A pMC encodes a family of MCs, where each member is obtained by replacing parameters with constants. The parameters allow encoding dependencies between transitions, which sets pMCs apart from interval MCs. The verification problem for pMCs asks whether each MC in the corresponding family satisfies a given temporal specification. The state-of-the-art approach for this problem is parameter lifting (PL) -- an abstraction-refinement loop that abstracts the pMC to a non-parametric model analyzed with standard probabilistic model checking techniques. This paper presents two key improvements to tackle the main limitations of PL. First, we introduce generalized parameter lifting (GPL) to lift various restrictive assumptions made by PL. Second, we present a big-step transformation algorithm that reduces parameter dependencies in pMCs and, therefore, results in tighter approximations. Experiments show that GPL is widely applicable and that the big-step transformation accelerates pMC verification by up to orders of magnitude.

Authors 5

1. Linus Heck

   Radboud University Nijmegen

   Affiliation as printed

   Radboud University , Nijmegen , the Netherlands

2. Tim Quatmann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

3. Jip Spel Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

4. Joost-Pieter Katoen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

5. Sebastian Junges

   Radboud University Nijmegen

   Affiliation as printed

   Radboud University , Nijmegen , the Netherlands