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Time to learn – learning timed automata from tests. (English) Zbl 1434.68212
André, Étienne (ed.) et al., Formal modeling and analysis of timed systems. 17th international conference, FORMATS 2019, Amsterdam, the Netherlands, August 27–29, 2019. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 11750, 216-235 (2019).
Summary: 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 novel 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 via testing. This generation is based on genetic programming, a search-based technique for automatic program creation. We evaluate our approach on 44 timed systems, comprised of four systems from the literature (two industrial and two academic) and 40 randomly generated examples.
For the entire collection see [Zbl 1428.68004].
MSC:
68Q32 Computational learning theory
68Q45 Formal languages and automata
Software:
EvoSuite; SFADiff; Uppaal
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