Title
Guided Invariant Model Checking Based on Abstraction and Symbolic Pattern Databases
Abstract
Arguably the two most important techniques that are used in model checking to counter the combinatorial explosion in the number of states are abstraction and guidance. In this work we combine these techniques in a natural way by using (homomorphic) abstractions that reveal an error in the model to guide the model checker in searching for the error state in the original system. The mechanism used to achieve this is based on pattern databases, commonly used in artificial intelligence. A pattern database represents an abstraction and is used as a heuristic to guide the search. In essence, therefore, the same abstraction is used to reduce the size of the model and guide a search algorithm. We implement this approach in NuSMV and evaluate it using 2 well-known circuit benchmarks. The results show that this method can outperform the original model checker by several orders of magnitude, in both time and space.
Year
DOI
Venue
2004
10.1007/978-3-540-24730-2_37
Lecture Notes in Computer Science
Keywords
Field
DocType
search algorithm,model checking,artificial intelligent
Transition system,Abstraction model checking,Heuristic,Model checking,Search algorithm,Computer science,Model-based reasoning,Theoretical computer science,Combinatorial explosion,Database,Symbolic trajectory evaluation
Conference
Volume
ISSN
Citations 
2988
0302-9743
32
PageRank 
References 
Authors
1.10
27
2
Name
Order
Citations
PageRank
Kairong Qian1633.38
Albert Nymeyer21069.98