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In directed model checking, the traversal of the state space is guided by an estimate of the distance from the current state to the nearest error state. This paper presents a distance-preserving abstraction for concurrent systems that allows one to compute an interesting estimate of the error distance without hitting the state explosion problem. Our… (More)

Stochastic branching processes are a classical model for describing random trees, which have applications in numerous fields including biology, physics, and natural language processing. In particular, they have recently been proposed to describe parallel programs with stochas-tic process creation. In this paper, we consider the problem of model checking… (More)

Systems and protocols combining concurrency and infinite state space occur quite often in practice, but are very difficult to verify automatically. At the same time, if the system is correct, it is desirable for a verifier to obtain not a simple " yes " answer, but some independently checkable certificate of correctness. We present SLAB — the first… (More)

UPPAAL/DMC is an extension of UPPAAL which provides generic heuristics for directed model checking. In this approach, the traversal of the state space is guided by a heuristic function which estimates the distance of a search state to the nearest error state. Our tool combines two recent approaches to design such estimation functions. Both are based on… (More)

Abstraction and slicing are both techniques for reducing the size of the state space to be inspected during verification. In this paper, we present a new model checking procedure for infinite-state concurrent systems that interleaves automatic abstraction refinement, which splits states according to new predicates obtained by Craig interpolation, with… (More)

We propose novel controller synthesis techniques for probabilistic systems modelled using stochastic two-player games: one player acts as a controller, the second represents its environment, and probability is used to capture uncertainty arising due to, for example, unreliable sensors or faulty system components. Our aim is to generate robust controllers… (More)

We consider models of programs that incorporate probability, dense real-time and data. We present a new abstraction refinement method for computing minimum and maximum reachability probabilities for such models. Our approach uses strictly local refinement steps to reduce both the size of abstractions generated and the complexity of operations needed, in… (More)

In this thesis, we introduce subsequence invariants, a new class of invariants for the specification and verification of systems. Unlike state invariants, which refer to the state variables of the system, subsequence invariants characterize the behavior of a concurrent system in terms of the occurrences of sequences of synchronization events. The first type… (More)

- Klaus Dräger, Vojtěch Forejt, Marta Kwiatkowska, David Parker, Mateusz Ujma
- 2014

We propose novel controller synthesis techniques for proba-bilistic systems modelled using stochastic two-player games: one player acts as a controller, the second represents its environment, and probability is used to capture uncertainty arising due to, for example, unreliable sensors or faulty system components. Our aim is to generate robust controllers… (More)