Abstract: Many hybrid systems can be conveniently modeled as Piecewise Affine Discrete Time Hybrid Systems PA-DTHS. As well known Bounded Model Checking (BMC) for such systems comes down to solve a Mixed Integer Linear Programming (MILP) feasibility problem. We present a SAT based BMC algorithm for automatic verification of PA-DTHSs. Using Counterexample Guided Abstraction Refinement (CEGAR) our algorithm gradually transforms a PA-DTHS verification problem into larger and larger SAT problems. Our experimental results show that our approach can handle PA-DTHSs that are more then 50 times larger than those that can be handled using a MILP solver.

Abstract: In this paper we present an explicit disk based verification algorithm for Probabilistic Systems defining discrete time/finite state Markov Chains. Given a Markov Chain and an integer k (horizon), our algorithm checks whether the probability of reaching an error state in at most k steps is below a given threshold. We present an implementation of our algorithm within a suitable extension of the Mur$\varphi$ verifier. We call the resulting probabilistic model checker FHP-Mur$\varphi$ (Finite Horizon Probabilistic Mur$\varphi$). We present experimental results comparing FHP-Mur$\varphi$ with (a finite horizon subset of) PRISM, a state-of-the-art symbolic model checker for Markov Chains. Our experimental results show that FHP-Mur$\varphi$ can handle systems that are out of reach for PRISM, namely those involving arithmetic operations on the state variables (e.g. hybrid systems).

Abstract: In this paper we present an explicit verification algorithm for Probabilistic Systems defining discrete time/finite state Markov Chains. We restrict ourselves to verification of Bounded PCTL formulas (BPCTL), that is, PCTL formulas in which all Until operators are bounded, possibly with different bounds. This means that we consider only paths (system runs) of bounded length. Given a Markov Chain $\cal M$ and a BPCTL formula Φ, our algorithm checks if Φ is satisfied in $\cal M$. This allows to verify important properties, such as reliability in Discrete Time Hybrid Systems. We present an implementation of our algorithm within a suitable extension of the Mur$\varphi$ verifier. We call FHP-Mur$\varphi$ (Finite Horizon Probabilistic Mur$\varphi$) such extension of the Mur$\varphi$ verifier. We give experimental results comparing FHP-Mur$\varphi$ with (a finite horizon subset of) PRISM, a state-of-the-art symbolic model checker for Markov Chains. Our experimental results show that FHP-Mur$\varphi$ can effectively handle verification of BPCTL formulas for systems that are out of reach for PRISM, namely those involving arithmetic operations on the state variables (e.g. hybrid systems).

Abstract: Many reactive systems are actually Stochastic Processes. Automatic analysis of such systems is usually very difficult thus typically one simplifies the analysis task by using simulation or by working on a simplified model (e.g. a Markov Chain). We present a Finite Horizon Probabilistic Model Checking approach which essentially can handle the same class of stochastic processes of a typical simulator. This yields easy modeling of the system to be analyzed together with formal verification capabilities. Our approach is based on a suitable disk based extension of the Mur$\varphi$ verifier. Moreover we present experimental results showing effectiveness of our approach.

Abstract: Abstract System level verification of cyber-physical systems has the goal of verifying that the whole (i.e., software + hardware) system meets the given specifications. Model checkers for hybrid systems cannot handle system level verification of actual systems. Thus, Hardware In the Loop Simulation (HILS) is currently the main workhorse for system level verification. By using model checking driven exhaustive HILS, System Level Formal Verification (SLFV) can be effectively carried out for actual systems. We present a parallel random exhaustive HILS based model checker for hybrid systems that, by simulating all operational scenarios exactly once in a uniform random order, is able to provide, at any time during the verification process, an upper bound to the probability that the System Under Verification exhibits an error in a yet-to-be-simulated scenario (Omission Probability). We show effectiveness of the proposed approach by presenting experimental results on SLFV of the Inverted Pendulum on a Cart and the Fuel Control System examples in the Simulink distribution. To the best of our knowledge, no previously published model checker can exhaustively verify hybrid systems of such a size and provide at any time an upper bound to the Omission Probability.