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Title: Testing and active learning of resettable finite-state machines
Author: Soucha, Michal
ISNI:       0000 0004 7964 5679
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2019
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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.
Supervisor: Bogdanov, Kirill ; Struth, Georg Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available