Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
BDD variable ordering for interacting finite state machines
DAC '94 Proceedings of the 31st annual Design Automation Conference
Binary decision diagrams and applications for VLSI CAD
Binary decision diagrams and applications for VLSI CAD
Improving the Variable Ordering of OBDDs Is NP-Complete
IEEE Transactions on Computers
Concurrency: state models & Java programs
Concurrency: state models & Java programs
Symbolic Model Checking
BDDs vs. Zero-Suppressed BDDs: for CTL Symbolic Model Checking of Petri Nets
FMCAD '96 Proceedings of the First International Conference on Formal Methods in Computer-Aided Design
RuleBase: Model Checking at IBM
CAV '97 Proceedings of the 9th International Conference on Computer Aided Verification
ZRES: The Old Davis-Putman Procedure Meets ZBDD
CADE-17 Proceedings of the 17th International Conference on Automated Deduction
FORCE: a fast and easy-to-implement variable-ordering heuristic
Proceedings of the 13th ACM Great Lakes symposium on VLSI
Comparing Finite-State Verification Techniques for Concurrent Software
Comparing Finite-State Verification Techniques for Concurrent Software
Flow analysis for verifying properties of concurrent software systems
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Finite-state verification (FSV) techniques attempt to prove properties about a model of a system by examining all possible behaviors of that model. This approach suffers from the state-explosion problem, where the size of the model or the analysis costs may be exponentially large with respect to the size of the system. Using symbolic data structures to represent subsets of the state space has been shown to usually be an effective optimization approach for hardware verification. The value for software verification, however, is still unclear. In this paper, we investigate applying two symbolic data structures, Binary Decision Diagrams (BDDs) and Zero-suppressed Binary Decision Diagrams (ZDDs), in two FSV tools, LTSA and FLAVERS. We describe an experiment showing that these two symbolic approaches can improve the performance of both FSV tools and are more efficient than two other algorithms that store the state space explicitly. Moreover, the ZDD-based approach often runs faster and can handle larger systems than the BDD-based approach.