Search algorithms for automated validation

A novel search technique called highway search is introduced. The search technique relies on a highway simulation which takes several homogeneous walks through a (possibly infinite) state space. Furthermore, we provide a memory-efficient algorithm that approximates a highway search and we prove that, under particular conditions, they coincide. The effectiveness of highway search is compared to two mainstream search techniques, viz. random search and randomised depth-first search. Our results demonstrate that randomised depth-first search explores the least amount of states in the effort of finding states of interest, whereas a highways search yields the shortest witnessing traces to such states

Search algorithms for automated validation

A novel search technique called highway search is introduced. The search technique relies on a highway simulation which takes several homogeneous walks through a (possibly infinite) state space. Furthermore, we provide a memory-efficient algorithm that approximates a highway search and we prove that, under particular conditions, they coincide. The effectiveness of highway search is compared to two mainstream search techniques, viz. random search and randomised depth-first search. Our results demonstrate that randomised depth-first search explores the least amount of states in the effort of finding states of interest, whereas a highways search yields the shortest witnessing traces to such states

Towards informed swarm verification

In this paper, we propose a new method to perform large scale grid model checking. A manager distributes the workload over many embarrassingly parallel jobs. Only little communication is needed between a worker and the manager, and only once the worker is ready for more work. The novelty here is that the individual jobs together form a so-called cumulatively exhaustive set, meaning that even though each job explores only a part of the state space, together, the tasks explore all states reachable from the initial state