Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Recursive hashing functions for n-grams
ACM Transactions on Information Systems (TOIS)
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Addressing dynamic issues of program model checking
SPIN '01 Proceedings of the 8th international SPIN workshop on Model checking of software
An analysis of bistate hashing
Proceedings of the Fifteenth IFIP WG6.1 International Symposium on Protocol Specification, Testing and Verification XV
Reliable Hashing without Collosion Detection
CAV '93 Proceedings of the 5th International Conference on Computer Aided Verification
Efficient randomized pattern-matching algorithms
IBM Journal of Research and Development - Mathematics and computing
Spin model checker, the: primer and reference manual
Spin model checker, the: primer and reference manual
An incremental heap canonicalization algorithm
SPIN'05 Proceedings of the 12th international conference on Model Checking Software
Model checking machine code with the GNU debugger
SPIN'05 Proceedings of the 12th international conference on Model Checking Software
SPIN '08 Proceedings of the 15th international workshop on Model Checking Software
Distributed LTL Model Checking with Hash Compaction
Electronic Notes in Theoretical Computer Science (ENTCS)
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Although computationaly neglegdible in other domains, the hashing of states can become one of the most expensive operations in software model checking. The reason lies in the potentially large state descriptions that programs expose. In this paper we introduce incremental hashing on large, dynamically changing state vectors that naturally arise during the verification of software in program model checkers. We exploit the fact that only small portions of the state description are changed by a single transition. Based on the changes in the predecessor state, the new state and its hash value can be computed efficiently.