Learning Finite State Models of Observable Nondeterministic Systems in a Testing Context

Khaled El-Fakih Roland Groz 1 Muhammad Naeem Irfan 1 Muzammil Shahbaz
1 VASCO
LIG - Laboratoire d'Informatique de Grenoble
Abstract : Learning models from test observations can be adapted to the case when the system provides nondeterministic answers. In this paper we propose an algorithm for inferring observable nondeterministic finite state machines (ONFSMs). The algorithm is based on Angluin L* algorithm for learning DFAs. We define rules for constructing and updating learning queries taking into account the properties of ONFSMs. Application examples, complexity analysis and an experimental evaluation of the proposed algorithm are provided.
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Communication dans un congrès
22nd IFIP International Conference on Testing Software and Systems, 2010, Natal, Brazil. pp.97-102, 2010
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https://hal.inria.fr/hal-00953395
Contributeur : Catherine Oriat <>
Soumis le : vendredi 28 février 2014 - 11:46:31
Dernière modification le : jeudi 11 janvier 2018 - 06:26:40

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  • HAL Id : hal-00953395, version 1

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Khaled El-Fakih, Roland Groz, Muhammad Naeem Irfan, Muzammil Shahbaz. Learning Finite State Models of Observable Nondeterministic Systems in a Testing Context. 22nd IFIP International Conference on Testing Software and Systems, 2010, Natal, Brazil. pp.97-102, 2010. 〈hal-00953395〉

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