Abstract
This work presents a novel approach for applying compositional model checking of behavioral UML models, based on learning. The Unified Modeling Language (UML) is a widely accepted modeling language for embedded and safety critical systems. As such the correct behavior of systems represented as UML models is crucial. Model checking is a successful automated verification technique for checking whether a system satisfies a desired property. However, its applicability is often impeded by its high time and memory requirements. A successful approach to tackle this limitation is compositional model checking. Recently, great advancements have been made in this direction via automatic learning-based Assume-Guarantee reasoning.
In this work we propose a framework for automatic Assume-Guarantee reasoning for behavioral UML systems. We apply an off-the-shelf learning algorithm for incrementally generating environment assumptions that guarantee satisfaction of the property. A unique feature of our approach is that the generated assumptions are UML state machines. Moreover, our Teacher works at the UML level: all queries from the learning algorithm are answered by generating and verifying behavioral UML systems.
An extended version including full proofs is published as a technical report in [22].
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Notes
- 1.
The case where several state machines run on the same thread is simpler, however presentation of both is cumbersome. We present only the more complex case.
- 2.
In the examples throughout the paper we assume EQs are implemented as FIFOs.
- 3.
In LTL, the syntax of this property is AGp. We choose to denote it by \(\forall Gp\) in order to differentiate the property from AG (which stands for Assume-Guarantee).
- 4.
It is ok to require p on a prefix leading to state err, since \(A_w\) is prefix closed for safety properties.
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Meller, Y., Grumberg, O., Yorav, K. (2016). Learning-Based Compositional Model Checking of Behavioral UML Systems. In: Braga, C., Ölveczky, P. (eds) Formal Aspects of Component Software. FACS 2015. Lecture Notes in Computer Science(), vol 9539. Springer, Cham. https://doi.org/10.1007/978-3-319-28934-2_15
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