Orion: A Technique to Prune State Space Search Directions for Guidance-Based Formal Verification

Abstract

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 simulation-based strategy combined with Bayesian modelling approach for finding effective guidance hints for state space traversal. A heuristic-based structural dependency of the design yields ineffective guidance hints which need further of filtering. To prune out the ineffective guidance hints, we first generate module-level sub-properties from static analysis of the design. These sub-properties and structural dependency-based guidance hints are analyzed in simulation traces generated from the constrained-random test benches. These conditional occurrence of sub-properties 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.