Design debugging is one of the most important steps in the modern integrated circuits (ICs) development cycle. Simulation-based verification is never sufficient for ensuring design correctness because of its incomplete nature. Formal techniques such as model checking promise to solve this issue through a complete state-space traversal approach. However, because of increasing design complexity, such methods suffer from scalability issues. Guidance-based state-space traversal techniques have been proposed in the past to assist the model checkers in overcoming the complexity bottleneck. Automatically identifying these guidance hints is relatively difficult and requires heuristic-based reasoning procedures. Additionally, to come up with quick fixes during the debug stage, an effective bug localization strategy is needed. In this article, we revisit the paradigm of guidance-based model checking and propose a methodology to improve these guidance generation mechanisms for achieving fine-grained bug localization. In particular, this work proposes a systematic methodology to localize the buggy RTL lines from the erroneous RTL simulation trace. The proposed technique involves the mining of invariant-like assertions from simulation traces. The mined assertions act as probable guidance candidates for the model checking exercise. To identify useful guidance hints from possible ones, we use the Bayesian networks that explore conditional dependence between the various hints at different levels and the target property. These guidance hints are utilized for obtaining possible buggy subregions, which are analyzed via an iterative model checking methodology for fine-grained bug localization. By using the proposed framework, bugs can be localized to within a few lines of RTL description.

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.