Learning Timed Automata via Genetic Programming
This work addresses the need for model learning in black-box systems with real-time constraints, offering an incremental advancement by extending existing techniques to timed automata.
The paper tackles the problem of learning timed automata from timed traces using genetic programming, achieving a method that generates models consistent with test data for 44 timed systems, including examples from literature and random cases.
Model learning has gained increasing interest in recent years. It derives behavioural models from test data of black-box systems. The main advantage offered by such techniques is that they enable model-based analysis without access to the internals of a system. Applications range from fully automated testing over model checking to system understanding. Current work focuses on learning variations of finite state machines. However, most techniques consider discrete time. In this paper, we present a method for learning timed automata, finite state machines extended with real-valued clocks. The learning method generates a model consistent with a set of timed traces collected by testing. This generation is based on genetic programming, a search-based technique for automatic program creation. We evaluate our approach on 44 timed systems, comprising four systems from the literature and 40 randomly generated examples.