A semantics for every GSPN

Generalised Stochastic Petri Nets (GSPNs) are a popular modelling formalism for performance and dependability analysis. Their semantics is traditionally associated to continuous-time Markov chains (CTMCs), enabling the use of standard CTMC analysis algorithms and software tools. Due to ambiguities in the semantic interpretation of confused GSPNs, this analysis strand is however restricted to nets that do not exhibit non-determinism, the so-called well-defined nets. This paper defines a simple semantics for every GSPN. No restrictions are imposed on the presence of confusions. Immediate transitions may be weighted but are not required to be. Cycles of immediate transitions are admitted too. The semantics is defined using a non-deterministic variant of CTMCs, referred to as Markov automata. We prove that for well-defined bounded nets, our semantics is weak bisimulation equivalent to the existing CTMC semantics. Finally, we briefly indicate how every bounded GSPN can be quantitatively assessed. © 2013 Springer-Verlag.Generalised Stochastic Petri Nets (GSPNs) are a popular modelling formalism for performance and dependability analysis. Their semantics is traditionally associated to continuous-time Markov chains (CTMCs), enabling the use of standard CTMC analysis algorithms and software tools. Due to ambiguities in the semantic interpretation of confused GSPNs, this analysis strand is however restricted to nets that do not exhibit non-determinism, the so-called well-defined nets. This paper defines a simple semantics for every GSPN. No restrictions are imposed on the presence of confusions. Immediate transitions may be weighted but are not required to be. Cycles of immediate transitions are admitted too. The semantics is defined using a non-deterministic variant of CTMCs, referred to as Markov automata. We prove that for well-defined bounded nets, our semantics is weak bisimulation equivalent to the existing CTMC semantics. Finally, we briefly indicate how every bounded GSPN can be quantitatively assessed. © 2013 Springer-Verlag

Incremental bisimulation abstraction refinement

Abstraction refinement techniques in probabilistic model checking are prominent approaches to the verification of very large or infinite-state probabilistic concurrent systems. At the core of the refinement step lies the implicit or explicit analysis of a counterexample. This paper proposes an abstraction refinement approach for the probabilistic computation tree logic (PCTL), which is based on incrementally computing a sequence of may- and must-quotient automata. These are induced by depth-bounded bisimulation equivalences of increasing depth. The approach is both sound and complete, since the equivalences converge to the genuine PCTL equivalence. Experimental results with a prototype implementation show the effectiveness of the approach. © 2013 IEEE.Abstraction refinement techniques in probabilistic model checking are prominent approaches to the verification of very large or infinite-state probabilistic concurrent systems. At the core of the refinement step lies the implicit or explicit analysis of a counterexample. This paper proposes an abstraction refinement approach for the probabilistic computation tree logic (PCTL), which is based on incrementally computing a sequence of may- and must-quotient automata. These are induced by depth-bounded bisimulation equivalences of increasing depth. The approach is both sound and complete, since the equivalences converge to the genuine PCTL equivalence. Experimental results with a prototype implementation show the effectiveness of the approach. © 2013 IEEE

LTL satisfiability checking revisited

We propose a novel algorithm for the satisfiability problem for Linear Temporal Logic (LTL). Existing approaches first transform the LTL formula into a B'uchi automaton and then perform an emptiness checking of the resulting automaton. Instead, our approach works on-the-fly by inspecting the formula directly, thus enabling finding a satisfying model quickly without constructing the full automaton. This makes our algorithm particularly fast for satisfiable formulas. We report on a prototype implementation, showing that our approach significantly outperforms state-of-the-art tools. © 2013 IEEE.We propose a novel algorithm for the satisfiability problem for Linear Temporal Logic (LTL). Existing approaches first transform the LTL formula into a B'uchi automaton and then perform an emptiness checking of the resulting automaton. Instead, our approach works on-the-fly by inspecting the formula directly, thus enabling finding a satisfying model quickly without constructing the full automaton. This makes our algorithm particularly fast for satisfiable formulas. We report on a prototype implementation, showing that our approach significantly outperforms state-of-the-art tools. © 2013 IEEE