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The problem of falsifying temporal logic properties of hybrid automata can be posed as a minimization problem by utilizing quantitative semantics for temporal logics. Previous work has used a variation of Simulated Annealing (SA) to solve the problem. While SA is known to converge to the global minimum of a continuous objective function over a closed and bounded search space, or when the search space is discrete, there do not exist convergence proofs for the cases addressed in that previous work. Namely, when the objective function is discontinuous, and when the objective is a vector-valued function. In this paper, we derive conditions and we prove convergence of SA to a global minimum in both scenarios. We also consider matters affecting the practical performance of SA.

We present a Monte-Carlo optimization technique for finding system behaviors that falsify a metric temporal logic (MTL) property. Our approach performs a random walk over the space of system inputs guided by a robustness metric defined by the MTL property. Robustness is guiding the search for a falsifying behavior by exploring trajectories with smaller robustness values. The resulting testing framework can be applied to a wide class of cyber-physical systems (CPS). We show through experiments on complex system models that using our framework can help automatically falsify properties with more consistency as compared to other means, such as uniform sampling.

The automatic analysis of transient properties of nonlinear dynamical systems is a challenging problem. The problem is even more challenging when complex state-space and timing requirements must be satisfied by the system. Such complex requirements can be captured by Metric Temporal Logic (MTL) specifications. The problem of finding system behaviors that do not satisfy an MTL specification is referred to as MTL falsification. This paper presents an approach for improving stochastic MTL falsification methods by performing local search in the set of initial conditions. In particular, MTL robustness quantifies how correct or wrong is a system trajectory with respect to an MTL specification. Positive values indicate satisfaction of the property while negative values indicate falsification. A stochastic falsification method attempts to minimize the system's robustness with respect to the MTL property. Given some arbitrary initial state, this paper presents a method to compute a descent direction in the set of initial conditions, such that the new system trajectory gets closer to the unsafe set of behaviors. This technique can be iterated in order to converge to a local minimum of the robustness landscape. The paper demonstrates the applicability of the method on some challenging nonlinear systems from the literature.

Relaxed notions of decidability widen the scope of automatic verification of hybrid systems. In quasi-decidability and $\delta$-decidability, the fundamental compromise is that if we are willing to accept a slight error in the algorithm's answer, or a slight restriction on the class of problems we verify, then it is possible to obtain practically useful answers. This paper explores the connections between relaxed decidability and the robust semantics of Metric Temporal Logic formulas. It establishes a formal equivalence between the robustness degree of MTL specifications, and the imprecision parameter $\delta$ used in $\delta$-decidability when it is used to verify MTL properties. We present an application of this result in the form of an algorithm that generates new constraints to the $\delta$-decision procedure from falsification runs, which speeds up the verification run. We then establish new conditions under which robust testing, based on the robust semantics of MTL, is in fact a quasi-semidecision procedure. These results allow us to delimit what is possible with fast, robustness-based methods, accelerate (near-)exhaustive verification, and further bridge the gap between verification and simulation.

Metric Temporal Logic (MTL) specifications can capture complex state and timing requirements. Given a nonlinear dynamical system and an MTL specification for that system, our goal is to find a trajectory that violates or satisfies the specification. This trajectory can be used as a concrete feedback to the system designer in the case of violation or as a trajectory to be tracked in the case of satisfaction. The search for such a trajectory is conducted over the space of initial conditions, system parameters and input signals. We convert the trajectory search problem into an optimization problem through MTL robust semantics. Robustness quantifies how close the trajectory is to violating or satisfying a specification. Starting from some arbitrary initial condition and parameter and given an input signal, we compute a descent direction in the search space, which leads to a trajectory that optimizes the MTL robustness. This process can be iterated to reach local optima (min or max). We demonstrate the method on examples from the literature.

In model-based design of cyber-physical systems, such as switched mixed-signal circuits or software-controlled physical systems,
it is common to develop a sequence of system models of different fidelity and complexity, each appropriate for a particular design or verification task.
In such a sequence, one model is often derived from the other by a process of simplification or implementation.
E.g. a Simulink model might be implemented on an embedded processor via automatic code generation.
Three questions naturally present themselves:
how do we quantify closeness between the two systems?
How can we measure such closeness?
If the original system satisfies some formal property, can we automatically infer what properties are then satisfied by the derived model?
This paper addresses all three questions:
we quantify the closeness between original and derived model via a distance measure between their outputs.
We then propose two computational methods for approximating this closeness measure.%, and demonstrate their use on several examples.
Finally, we derive syntactical re-writing rules which, when applied to a Metric Temporal Logic specification satisfied by the original model, produce a formula satisfied by the derived model.
We demonstrate the soundness of the theory with several experiments.

Motivated by the Model-Based Design process for Cyber-Physical Systems, we
consider issues in conformance testing of systems. Conformance is a
quantitative notion of similarity between the output trajectories of systems,
which considers both temporal and spatial aspects of the outputs. Previous work
developed algorithms for computing the conformance degree between two systems,
and demonstrated how formal verification results for one system can be re-used
for a system that is conformant to it. In this paper, we study the relation
between conformance and a generalized approximate simulation relation for the
class of Open Metric Transition Systems (OMTS). This allows us to prove a
small-gain theorem for OMTS, which gives sufficient conditions under which the
feedback interconnection of systems respects the conformance relation, thus
allowing the building of more complex systems from conformant components.

Relaxed notions of decidability widen the scope of automatic verification of hybrid systems. In quasi-decidability and Î´-decidability, the fundamental compromise is that if we are willing to accept a slight error in the algorithm's answer, or a slight restriction on the class of problems we verify, then it is possible to obtain practically useful answers. This paper explores the connections between relaxed decidability and the robust semantics of Metric Temporal Logic formulas. It establishes a formal equivalence between the robustness degree of MTL specifications, and the imprecision parameter Î´ used in Î´-decidability when it is used to verify MTL properties. We present an application of this result in the form of an algorithm that generates new constraints to the Î´-decision procedure from falsification runs, which speeds up the verification run. We then establish new conditions under which robust testing, based on the robust semantics of MTL, is in fact a quasi-semidecision procedure. These results allow us to delimit what is possible with fast, robustness-based methods, accelerate (near-)exhaustive verification, and further bridge the gap between verification and simulation.