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Model checking is one of the most efficient techniques in software system verification. However, state space explosion is a big challenge while using this technique to check different properties like safety ones. In this situation, one can search the state space to find a reachable state in which the safety property is violated. Hence, reachability checking can be done instead of checking safety property. However, checking reachability in the worst case may cause state space explosion again. To handle this problem, our idea is based on generating a small model consistent with the main model. Then by exploring the state space entirely, we search it to find the goal states. After finding the goal states, we label the paths which start from the initial state and leading to a goal state. Then using the ensemble classification technique, the necessary knowledge is extracted from these paths to intelligently explore the state space of the bigger model. Ensemble machine learning technique uses Boosting method along with decision trees. It follows sampling techniques by replacement. This method generates k predictive models after sampling k times. Finally, it uses a voting mechanism to predict the labels of the final path. Our proposed approach is implemented in GROOVE, which is an open source toolset for designing and model checking graph transformation systems. Our experiments show a significant improvement in terms of both speed and memory usage. In average, our approach consumes nearly 42% fewer memory than other approaches. Also, it generates witnesses nearly 20% shorter than others, in average. [ABSTRACT FROM AUTHOR]

Bagging and Boosting are two main ensemble approaches consolidating the decisions of several hypotheses. The diversity of the ensemble members is considered to be a significant element to obtain generalization error. Here, an inventive method called EBAGTS (ensemble-based artificially generated training samples) is proposed to generate ensembles. It manipulates training examples in three ways in order to build various hypotheses straightforwardly: drawing a sub-sample from training set, reducing/raising error-prone training instances, and reducing/raising local instances around error-prone regions. The proposed method is a straightforward, generic framework utilizing any base classifier as its ensemble members to assemble a powerfully built combinational classifier. Decision-tree classifier and multilayer perceptron classifier as some basic classifiers have been employed in the experiments to indicate the proposed method accomplish higher predictive accuracy compared to meta-learning algorithms like Boosting and Bagging. Furthermore, EBAGTS outperforms Boosting more impressively as the training data set gets broader. It is illustrated that EBAGTS can fulfill better performance comparing to the state of the art. [ABSTRACT FROM AUTHOR]