Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification---a methodology of search-based testing that employs stochastic optimization---is attracting attention as an alternative quality assurance method. Inspired by the recent works that advocate coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations. MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner.
@article{arxiv.1803.06276,
title = {Two-Layered Falsification of Hybrid Systems guided by Monte Carlo Tree Search},
author = {Zhenya Zhang and Gidon Ernst and Sean Sedwards and Paolo Arcaini and Ichiro Hasuo},
journal= {arXiv preprint arXiv:1803.06276},
year = {2018}
}