Using Features Of Models To Improve State Space Exploration

State space methods are a popular approach to perform formal verification. However, these methods suffer from the state space explosion problem. In the past decades many methods did arise to cope with the state spaces of larger models. As result, a user has many different strategies in which state space methods can be applied on new models. Due to the wide variety of strategies and models is may be hard for a user to select an appropriate strategy. If a bad strategy is selected, the given model can be unsolvable or the process may waste resources like time and memory. Moreover, the intervention of the user makes state space methods less automated. Therefore, it would be convenient if model checking tools itself determine the strategy for a given model. In this way, model checking tools can determine the most suitable strategy for a given model such that the available resources are optimally utilized. This process requires model checking tools to predict a strategy based on the information presented in a given model. Our research investigates to what extent characteristics of a model can be used to predict an appropriate strategy. The performance of 784 different PNML and DVE models was determined using LTSmin for 60 selected strategies. This information was used to create several classifiers using machine learning techniques. The classifiers should predict an appropriate strategy given eleven selected features of a model. The performance data of the models show that each strategy has some set of models it is not appropriate for. None of the strategies was able to solve all models. Hence, for a given set of models a dynamic selection of the strategy is recommended. Unfortunately, the classifiers did not outperform all of the strategies. But each of the examined features did contribute in providing useful information for predicting an appropriate strategy.