Directed and heuristic counterexample generation for probabilistic model checking

Abstract

The generation of counterexamples for probabilistic model checking has been an area of active research over the past five years. Tangible outcome of this research are novel directed and heuristic algorithms for efficient generation of probabilistic counterexamples, such as K* and XBF. In this paper we present an empirical evaluation of the efficiency of these algorithms and the well-known Eppstein's algorithm. We will also evaluate the effect of optimisations applied to Eppstein, K* and XBF. Additionally, we will show, how information produced during model checking can be used to guide the search for counterexamples. This is a first step towards automatically generating heuristic functions. The experimental evaluation of the various algorithms is done by applying them to one case study, knwon from the literature on probabilistic model checking and one case study taken from the automotive industry.