Soucha, M. and Bogdanov, K. (2020) State identification sequences from the splitting tree. Information and Software Technology, 123. 106297. ISSN 0950-5849
Abstract
Context:Software testing based on finite-state machines.
Objective:Improving the performance of existing testing methods by construction of more efficient separating sequences, so that states entered by a system under test can be identified in a much shorter span of time.
Method: This paper proposes an efficient way to construct separating sequences for subsets of states for any deterministic finite-state machine. It extends an existing algorithm that builds an adaptive distinguishing sequence (ADS) from a splitting tree to machines that do not possess an ADS. Our extension to this construction algorithm allows one not only to construct a separating sequence for any subset of states but also form sets of separating sequences, such as harmonized state identifiers (HSI) and incomplete adaptive distinguishing sequences, that are used by efficient testing and learning algorithms.
Results: The experiments confirm that the length and number of test sequences produced by testing methods that use HSIs constructed by our extension is significantly improved.
Conclusion:By constructing more efficient separating sequences the performance of existing test methods significantly improves.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier. This is an author produced version of a paper subsequently published in Information and Software Technology. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Splitting tree; Separating sequence; Harmonized state identifiers; Adaptive distinguishing sequence; Finite-state machine; Software testing; Regular inference |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 17 Mar 2020 07:37 |
Last Modified: | 06 Dec 2021 17:16 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.infsof.2020.106297 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158336 |