Orion: A Technique to Prune State Space Search Directions for GuidanceBased Formal Verification Model checking of large designs is a challenging task because of different scalability issues. In this paper, we aim to utilize guided state space traversal to address this issue. However, providing guidance for state space traversal of complex designs is also an equally challenging problem. We adopt a simulationbased strategy combined with Bayesian modelling approach for finding effective guidance hints for state space traversal. A heuristicbased structural dependency of the design yields ineffective guidance hints which need further of filtering. To prune out the ineffective guidance hints, we first generate modulelevel subproperties from static analysis of the design. These subproperties and structural dependencybased guidance hints are analyzed in simulation traces generated from the constrainedrandom test benches. These conditional occurrence of subproperties and guidance hints are inputs to a Bayesian model which can then provide us the guidance hints with the highest profitability. With the proposed methodology, we succeed in pruning out the set of unprofitable guidance hints and obtain effective search directions which are then used to assist the model checking procedure. Experiments on two complex designs for different properties show the effectiveness of the proposed methodology in reducing CPU time during model checking.

Generalized PropertyDirected Reachability for Hybrid Systems Generalized propertydirected reachability (GPDR) belongs to the family of the modelchecking techniques called IC3PDR. It has been successfully applied to software verification; for example, it is the core of Spacer, a stateoftheart Hornclause solver bundled with Z3. However, it has yet to be applied to hybrid systems, which involve a continuous evolution of values over time. As the first step towards GPDRbased model checking for hybrid systems, this paper formalizes (textsc HGPDR), an adaptation of GPDR to hybrid systems, and proves its soundness. We also implemented a semiautomated proofofconcept verifier, which allows a user to provide hints to guide verification steps.