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Online monitoring is a popular validation approach in which the temporal behavior of a system is checked to assess whether it satisfies a given specification expressed, e.g., in signal temporal logic (STL). This is done by employing a monitor that, at each time point, states the specification validity: satisfied, violated, or unknown. In some settings, monitoring should continue even after a violation episode is detected, to detect possible future violation episodes. However, for a monitor just relying on STL semantics, this is not possible, as, once the specification is violated by an input signal, any continuation of the signal still violates the specification. To tackle this problem, we here propose an optimal reset technique that, at runtime, detects the end of a violation episode and shifts the evaluation of the monitor to skip such an episode. In this way, the monitoring can continue to detect possible other future violation episodes. We propose a framework that integrates the reset technique with an existing monitoring approach. Experiments on two Simulink models show that the technique can effectively reset the monitor and report all the violation episodes, with a negligible overhead on the monitoring cost. [ABSTRACT FROM AUTHOR]

The verification of hybrid systems is intrinsically hard, due to the continuous dynamics that leads to infinite search spaces. Therefore, research attempts focused on hybrid system falsification of a black-box model, a technique that aims at finding an input signal violating the desired temporal specification. Main falsification approaches are based on stochastic hill-climbing optimization, that tries to minimize the degree of satisfaction of the temporal specification, given by its robust semantics. However, in the presence of constraints between the inputs, these methods become less effective. In this article, we solve this problem using a search space transformation that first maps points of the unconstrained search space to points of the constrained one, and then defines the fitness of the former ones based on the robustness values of the latter ones. Based on this search space transformation, we propose a falsification approach that performs the search over the unconstrained space, guided by the robustness of the mapped points in the constrained space. We introduce three versions of the proposed approach that differ in the way of selecting the mapped points. Experiments show that the proposed approach outperforms state-of-the-art constrained falsification approaches. [ABSTRACT FROM AUTHOR]

Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification—a methodology of search-based testing that employs stochastic optimization—is thus attracting attention as an alternative quality assurance method. Inspired by the recent work that advocates coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations (e.g., in computer Go). MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner. We demonstrate the proposed framework through experiments with benchmarks from the automotive domain. [ABSTRACT FROM AUTHOR]