Constraint-driven nonlinear reachability analysis with automated tuning of tool properties

The effectiveness of reachability analysis often depends on choosing appropriate values for a set of tool-specific properties which need to be manually tailored to the specific system involved and the reachable set to be evolved. Such property tuning is a time-consuming task, especially when dealing with nonlinear systems. In this paper, we propose, instead, a methodology to automatically and dynamically choose property values for reachability analysis along the system evolution, based on the actual verification objective, i.e., the verification or falsification of a set of constraints. By leveraging an initial solution to the reachable set, we estimate bounds on the numerical accuracy required from each integration step to provide a definite answer to the satisfaction of the constraints. Based on these accuracy bounds, we design a cost function which we use, after mapping the property space to an integer space, to search for locally optimal property values that yield the desired accuracy. Results from the application of our methodology to the nonlinear reachability analysis tool ARIADNE show that the frequency of correct answers to constraint satisfaction problems increases significantly with respect to a manual approach.