Exploiting Transition Locality in the Disk Based Mur phi Verifier
FMCAD '02 Proceedings of the 4th International Conference on Formal Methods in Computer-Aided Design
Exploiting Transition Locality in Automatic Verification
CHARME '01 Proceedings of the 11th IFIP WG 10.5 Advanced Research Working Conference on Correct Hardware Design and Verification Methods
Using Magnatic Disk Instead of Main Memory in the Murphi Verifier
CAV '98 Proceedings of the 10th International Conference on Computer Aided Verification
Best-first frontier search with delayed duplicate detection
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Structured duplicate detection in external-memory graph search
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Domain-independent structured duplicate detection
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Large-scale parallel breadth-first search
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Edge partitioning in external-memory graph search
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Delayed duplicate detection: extended abstract
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
"To store or not to store" reloaded: reclaiming memory on demand
FMICS'06/PDMC'06 Proceedings of the 11th international workshop, FMICS 2006 and 5th international workshop, PDMC conference on Formal methods: Applications and technology
BEEM: benchmarks for explicit model checkers
Proceedings of the 14th international SPIN conference on Model checking software
Revisiting resistance speeds up I/O-efficient LTL model checking
TACAS'08/ETAPS'08 Proceedings of the Theory and practice of software, 14th international conference on Tools and algorithms for the construction and analysis of systems
I/O efficient directed model checking
VMCAI'05 Proceedings of the 6th international conference on Verification, Model Checking, and Abstract Interpretation
Time-Efficient model checking with magnetic disk
TACAS'05 Proceedings of the 11th international conference on Tools and Algorithms for the Construction and Analysis of Systems
Parallel external directed model checking with linear i/o
VMCAI'06 Proceedings of the 7th international conference on Verification, Model Checking, and Abstract Interpretation
Survey on Directed Model Checking
Model Checking and Artificial Intelligence
ASAP: An Extensible Platform for State Space Analysis
PETRI NETS '09 Proceedings of the 30th International Conference on Applications and Theory of Petri Nets
Dynamic State Space Partitioning for External Memory Model Checking
FMICS '09 Proceedings of the 14th International Workshop on Formal Methods for Industrial Critical Systems
Flash memory efficient LTL model checking
Science of Computer Programming
Efficient explicit-state model checking on general purpose graphics processors
SPIN'10 Proceedings of the 17th international SPIN conference on Model checking software
A graphical approach to component-based and extensible model checking platforms
Transactions on Petri Nets and Other Models of Concurrency V
Combining the sweep-line method with the use of an external-memory priority queue
SPIN'12 Proceedings of the 19th international conference on Model Checking Software
Dynamic state space partitioning for external memory state space exploration
Science of Computer Programming
| Hi-index | 0.00 |
Duplicate detection is an expensive operation of disk-based model checkers. It consists of comparing some potentially new states, the candidatestates, to previous visitedstates. We propose a new approach to this technique called dynamic delayed duplicate detection. This one exploits some typical properties of states spaces, and adapts itself to the structure of the state space to dynamically decide when duplicate detection must be conducted. We implemented this method in a new algorithm and found out that it greatly cuts down the cost of duplicate detection. On some classes of models, it performs significantly better than some previously published algorithms.