Time to Learn - Learning Timed Automata from Tests

Martin Tappler, Bernhard K. Aichernig, Kim Guldstrand Larsen, Florian Lorber

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

12 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationFormal Modeling and Analysis of Timed Systems - 17th International Conference, FORMATS 2019, Proceedings
EditorsÉtienne André, Mariëlle Stoelinga, Mariëlle Stoelinga
Number of pages20
Place of PublicationHeidelberg
Publication dateAug 2019
ISBN (Print)978-3-030-29661-2
ISBN (Electronic)978-3-030-29662-9
Publication statusPublished - Aug 2019
Event17th FORMATS 2019: Amsterdam, The Netherlands - Amsterdam, Netherlands
Duration: 26 Aug 201931 Aug 2019


Conference17th FORMATS 2019: Amsterdam, The Netherlands
SeriesLecture Notes in Computer Science


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