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
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
Applying SAT Methods in Unbounded Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
SAT Based Abstraction-Refinement Using ILP and Machine Learning Techniques
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
NuSMV 2: An OpenSource Tool for Symbolic Model Checking
CAV '02 Proceedings of the 14th International Conference on Computer Aided Verification
SAT-based unbounded symbolic model checking
Proceedings of the 40th annual Design Automation Conference
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Multiple-counterexample guided iterative abstraction refinement: an industrial evaluation
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
Explaining Counterexamples Using Causality
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Explaining counterexamples using causality
Formal Methods in System Design
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It is a hot research topic to eliminate irrelevant variables from counterexample, to make it easier to be understood. The BFL algorithm is the most effective counterexample minimization algorithm compared to all other approaches. But its time overhead is very large due to one call to SAT solver for each candidate variable to be eliminated. The key to reduce time overhead is to eliminate multiple variables simultaneously. Therefore, we propose a faster counterexample minimization algorithm based on refutation analysis in this paper. We perform refutation analysis on those UNSAT instances of BFL, to extract the set of variables that lead to UNSAT. All variables not belong to this set can be eliminated simultaneously as irrelevant variables. Thus we can eliminate multiple variables with only one call to SAT solver. Theoretical analysis and experiment result shows that, our algorithm can be 2 to 3 orders of magnitude faster than existing BFL algorithm, and with only minor lost in counterexample minimization ability.