Computer-aided verification of coordinating processes: the automata-theoretic approach
Computer-aided verification of coordinating processes: the automata-theoretic approach
POPL '02 Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Counterexample-guided choice of projections in approximate symbolic model checking
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
Counterexample-Guided Abstraction Refinement
CAV '00 Proceedings of the 12th International Conference on Computer Aided Verification
Refining the SAT decision ordering for bounded model checking
Proceedings of the 41st annual Design Automation Conference
Iterative Abstraction using SAT-based BMC with Proof Analysis
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Automatic abstraction without counterexamples
TACAS'03 Proceedings of the 9th international conference on Tools and algorithms for the construction and analysis of systems
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
Abstraction refinement for bounded model checking
CAV'05 Proceedings of the 17th international conference on Computer Aided Verification
SAT-based counterexample-guided abstraction refinement
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Verification of complex system-on-a-chip (SoC) designs becomes a critical problem in practice. We consider using model checking to verify the correctness of such systems. We study the state separation problem in the framework of counterexample-guided abstraction refinement. We present two fast heuristics to solve this problem. To the best of our knowledge, our work is the first study on the effectiveness of greedy heuristics for this problem. In comparison with the latest work using the decision tree learning (DTL) solver, the proposed method performs about three orders of magnitude faster and the size of the separation set is 70% smaller on average.