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Testing and Active Learning of Resettable Finite-State Machines

Soucha, Michal (2019) Testing and Active Learning of Resettable Finite-State Machines. PhD thesis, University of Sheffield.

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This thesis proposes novel active-learning algorithms and testing methods for deterministic finite-state machines that (i) have a specified transition from every state on each input of the (fixed) alphabet and (ii) can be reliably reset to the initial state on request. These algorithms rely on the novel methods of construction of separating sequences. Extensive evaluation demonstrates that the described testing and learning methods are the most efficient in terms of the amount of interaction by a tester with the system under test.

Item Type: Thesis (PhD)
Additional Information: Author's library FSMlib for handling Finite-State Machines, their testing and learning is available at https://github.com/Soucha/FSMlib and machines with experiments' results at https://github.com/Soucha/FSMmodels/tree/ExperimentsThesis2017
Keywords: finite-state machine, separating sequence, software testing, active learning, automata inference, deterministic finite acceptor, Mealy machine, Moore machine
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Computer Science (Sheffield)
The University of Sheffield > Faculty of Science (Sheffield) > Computer Science (Sheffield)
Identification Number/EthosID: uk.bl.ethos.778840
Depositing User: Ing. Michal Soucha
Date Deposited: 16 Jul 2019 13:21
Last Modified: 25 Sep 2019 20:08
URI: http://etheses.whiterose.ac.uk/id/eprint/24370

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