Abstract
Active automata learning (AAL) algorithms infer accurate automata models of black box applications, letting developers verify the behavior of increasingly complex real-time systems (RTS). However, learning models of larger RTS often takes very long or is not feasible at all. We introduce Mealy machines with local timers, a new class of Mealy machines that permit multiple location-bound timers and that can be learned efficiently. We design an efficient learning algorithm for them and validate our method across diverse case studies ranging from automotive systems to smart home appliances, where we drastically reduce runtimes compared to the state-of-the-art approach, thus, making AAL available for a wide range of RTS.
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Change history
09 November 2023
A correction has been published.
Notes
- 1.
- 2.
As in timed automata, locations in MMLTs can represent multiple system states.
- 3.
We usually abort an observation after some maximum waiting time \(\varDelta \) during learning, thus, giving these transitions the output \((\varDelta , void)\) in practice.
- 4.
We omit this loop in Fig. 1b for readability because its output is silent.
- 5.
Models and MMLT learner available at git.tu-berlin.de/pkogel/mmlt-learning.
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Kogel, P., Klös, V., Glesner, S. (2023). Learning Mealy Machines with Local Timers. In: Li, Y., Tahar, S. (eds) Formal Methods and Software Engineering. ICFEM 2023. Lecture Notes in Computer Science, vol 14308. Springer, Singapore. https://doi.org/10.1007/978-981-99-7584-6_4
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