Verifying Complex Systems Probabilistically through Learning, Abstraction and Refinement.
Jingyi Wang, Jun Sun, Shengchao Qin · arXiv (Cornell University) · 2016
Precisely modeling complex systems like cyber-physical systems is often challenging, which may render model-based system verification techniques like model checking infeasible. To overcome this challenge, we propose a method called LAR to `verify' such complex systems through a combination of learning, abstraction and refinement. Instead of starting with system modeling, our method takes a set of concrete system traces as input. The output is either a counterexample with a bounded probability of being a spurious counterexample, or a probabilistic model based on which the given property is `verified'. The model could be viewed as a proof obligation, i.e., the property is verified if the model is correct. It can also be used for subsequent system analysis activities like runtime monitoring. Our method has been implemented as a self-contained software toolkit. The evaluation on multiple benchmark systems as well as a real-world water purification system show promising results.