Finding safety errors with ACO

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Model Checking is a well-known and fully automatic technique forchecking software properties, usually given as temporal logicformulae on the program variables. Most model checkers found inthe literature use exact deterministic algorithms to check theproperties. These algorithms usually require huge amounts ofcomputational resources if the checked model is large. We proposehere the use of a new kind of Ant Colony Optimization (ACO) model, ACOhg, to refute safety properties in concurrent systems. ACO algorithms are stochastic techniques belonging to the class of metaheuristic algorithms and inspired by the foraging behaviour of real ants. The traditional ACO algorithms cannot deal with the model checking problem and thus we use ACOhg to tackle it. The results state that ACOhg algorithms find optimal or near optimal error trails in faulty concurrent systems with a reduced amount of resources, outperforming algorithms that are the state-of-the-art in model checking. This fact makes them suitable for checking safety properties in large concurrent systems, in which traditional techniques fail to find errors because of the model size.