Integrating model verification and self-adaptation

In software development, formal verification plays an important role in improving the quality and safety of products and processes. Model checking is a successful approach to verification, used both in academic research and industrial applications. One important improvement regarding utilization of model checking is the development of automated processes to evolve models according to information obtained from verification. In this paper, we propose a new framework that make use of artificial intelligence and machine learning to generate and evolve models from partial descriptions and examples created by the model checking process. This was implemented as a tool that is integrated with a model checker. Our work extends model checking to be applicable when initial description of a system is not available, through observation of actual behaviour of this system. The framework is capable of integrated verification and evolution of abstract models, but also of reengineering partial models of a system.