Applying Automata Learning to Embedded Control Software

Organisation:	Oce Technologies B.V. and Radboud University Nijmegen (THE NETHERLANDS)
Method:	Mealy machines
Tools used:	LearnLib CADP (Construction and Analysis of Distributed Processes)
Domain:	Embedded control software.
Period:	2015
Size:	3410 states, 262570 transitions.
Description:	Automata Learning consists in constructing a model of the behaviour of a software component from observations. In this paper, automata learning, and in particular the state-of-the-art automata learning tool LearnLib, is used to construct a behavioural model of the ESM (Engine Status Manager), a piece of control software that is used in many printers and copiers of Oce. The aim is, e.g., to use the learned model for regression testing of future versions of the software. To verify the correctness of the model learned automatically from the ESM using LearnLib, its equivalence was checked with a model that was generated directly from the code of the ESM itself. To this aim, a transformation was devised from the RRRT (Rational Rose RealTime) code of the ESM to an automaton in the .aut format. The learned automaton was also transformed into the .aut format and both automata were compared using a bisimulation checker of CADP.
Conclusions:	This work shows that LearnLib is effective in learning a model of an industrial software component. The use of CADP has allowed to verify the equivalence between the learned model and the model derived from the RRRT implementation.
Publications:	[Smeenk-Moerman-Vaandrager-Jansen-15] Wouter Smeenk, Joshua Moerman, Frits Vaandrager, and David Jansen. "Applying Automata Learning to Embedded Control Software." In Proceedings of the 17th International Conference on Formal Engineering Methods ICFEM'2015, LNCS volume 9407, pages 67-83. Springer-Verlag, November 2015. Available on-line at: http://sws.cs.ru.nl/publications/papers/fvaan/ESM/main.pdf or from the CADP Web site in PDF or PostScript [Moerman-19] Joshua Moerman. "Nominal Techniques and Black Box Testing for Automata Learning". PhD thesis, Radboud University, Nijmegen, The Netherlands, July 2019. Available on-line at: https://repository.ubn.ru.nl/bitstream/handle/2066/204194/204194.pdf or from the CADP Web site in PDF or PostScript
Contact:	Frits Vaandrager Faculty of Science Radboud University Nijmegen P.O. Box 9010, 6500 GL Nijmegen THE NETHERLANDS Email: F.Vaandrager@cs.ru.nl
Further remarks:	This case study, amongst others, is described on the CADP Web site: http://cadp.inria.fr/case-studies

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