Name
Abstract | ||
|---|---|---|
Model checking for large-scale systems is extremely difficult due to the state explosion problem. Creating useful abstractions for model checking task is a challenging problem, often involving many iterations of refinement. In this paper we consider techniques for model checking in the counterexample-guided abstraction refinement. The state separation problem is one popular approach in counterexample-guided abstraction refinement, and it poses the main hurdle during the refinement process. To achieve effective minimization of the separation set, we present a novel probabilistic learning approach based on the sample learning technique, evolutionary algorithm, and effective heuristics. We integrate it with the abstraction refinement framework in the VIS [1] model checker. We include experimental results on model checking to compare our new approach to recently published techniques. The benchmark results show that our approach has overall speedup of more than 56 percent against previous techniques. Our work is the first successful integration of evolutionary algorithm and abstraction refinement for model checking. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TC.2009.105 | IEEE Trans. Computers |
Keywords | Field | DocType |
abstraction refinement,popular approach,counterexample-guided abstraction refinement,model checking,evolutionary algorithm,new approach,model checking task,model checker,refinement process,integrating evolutionary computation,abstraction refinement framework,verification,decision trees,evolutionary computation,probabilistic logic,computational modeling,formal verification,learning artificial intelligence,probability,evolutionary computing,data mining,mathematical model | Abstraction model checking,Model checking,Evolutionary algorithm,Computer science,Evolutionary computation,Theoretical computer science,Heuristics,Probabilistic logic,Formal verification,Speedup | Journal |
Volume | Issue | ISSN |
59 | 1 | 0018-9340 |
Citations | PageRank | References |
11 | 0.60 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei He | 1 | 175 | 28.32 |
| Xiaoyu Song | 2 | 318 | 46.99 |
| William N. N. Hung | 3 | 304 | 34.98 |
| Ming Gu | 4 | 554 | 74.82 |
| Jia-guang Sun | 5 | 1807 | 134.30 |