Groz, R., Oriat, C., Vega, G. et al. (3 more authors) (2023) Active inference of extended finite state models of software systems. In: Proceedings of Machine Learning Research. International Conference on Grammatical Inference, 10-13 Jul 2023, Rabat, Morocco. Proceedings of Machine Learning Research , pp. 265-269.
Abstract
Extended finite state machines (EFSMs) model stateful systems with internal data variables, and have many software engineering applications. It is possible to infer such models by observing system behaviour. Still, existing approaches are either limited to classical FSM models with no internal data state, or implicitly require the ability to reset the system under inference, which may not always be possible. We present an extension to the hW-inference algorithm that can infer EFSM models, with input and output parameters as well as guards and internal registers and their data update functions, from systems without a reliable reset. For the problem to be tractable, we require some assumptions on the observability and determinism of the system. The main restriction is that the control flow of the system must be finite, although data types could be infinite.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Author(s) |
Keywords: | Query learning; Extended Automata; 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) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 30 Nov 2023 14:42 |
Last Modified: | 30 Nov 2023 14:42 |
Status: | Published |
Publisher: | Proceedings of Machine Learning Research |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:206029 |