Symbolic model checking using SAT procedures instead of BDDs
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Fate and Free Will in Error Traces
TACAS '02 Proceedings of the 8th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
NuSMV 2: An OpenSource Tool for Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
Simulation-based bug trace minimization with BMC-based refinement
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
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It is a hotly research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. BFL algorithm is the most effective Counterexample minimization algorithm compared to all other approaches, but its run time overhead is very large due to one call to SAT solver per candidate variable to be eliminated. So we propose a faster counterexample minimization algorithm based on refutation analysis and incremental SAT. First, for every UNSAT instance of BFL, we perform refutation analysis to extract the set of variables that lead to UNSAT, all variables not belong to this set can be eliminated simultaneously. In this way, we can eliminate many variables with only one call to SAT solver. At the same time, we employ incremental SAT approach to share learned clauses between similar instances of BFL, to prevent overlapped state space from being searched repeatedly. Theoretic analysis and experiment result shows that, our approach can be 1 to 2 orders of magnitude faster than BFL, and still retain the minimization ability of BFL.