Foster, M. orcid.org/0000-0001-8233-9873, Derrick, J. and Walkinshaw, N. (2022) Reverse-engineering EFSMs with data dependencies. In: Testing Software and Systems. ICTSS 2021 : The 33rd IFIP International Conference on Testing Software and Systems, 10-11 Nov 2021, Virtual conference. Lecture Notes in Computer Science . Springer Nature , pp. 37-54. ISBN 9783031046728
Abstract
EFSMs provide a way to model systems with internal data variables. In situations where they do not already exist, we need to infer them from system behaviour. A key challenge here is inferring the functions which relate inputs, outputs, and internal variables. Existing approaches either work with white-box traces, which expose variable values, or rely upon the user to provide heuristics to recognise and generalise particular data-usage patterns. This paper presents a preprocessing technique for the inference process which generalises the concrete values from the traces into symbolic functions which calculate output from input, even when this depends on values not present in the original traces. Our results show that our technique leads to more accurate models than are produced by the current state-of-the-art and that somewhat accurate models can still be inferred even when the output of particular transitions depends on values not present in the original traces.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IFIP International Federation for Information Processing. This is an author-produced version of a paper subsequently published in Testing Software and Systems 33rd IFIP WG 6.1 International Conference, ICTSS 2021, London, UK, November 10–12, 2021, Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | EFSM Inference; Model Inference; Genetic Programming |
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) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council EP/T030526/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Sep 2021 09:37 |
Last Modified: | 10 May 2022 12:26 |
Status: | Published |
Publisher: | Springer Nature |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-031-04673-5_3 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177494 |