Active Inference of Extended Finite State Models of Software Systems

Roland Groz, Catherine Oriat, Germán Vega, Adenilso Simao, Michael Foster, Neil Walkinshaw
Proceedings of 16th edition of the International Conference on Grammatical Inference, PMLR 217:265-269, 2023.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v217-groz23a, title = {Active Inference of Extended Finite State Models of Software Systems}, author = {Groz, Roland and Oriat, Catherine and Vega, Germ\'an and Simao, Adenilso and Foster, Michael and Walkinshaw, Neil}, booktitle = {Proceedings of 16th edition of the International Conference on Grammatical Inference}, pages = {265--269}, year = {2023}, editor = {Coste, François and Ouardi, Faissal and Rabusseau, Guillaume}, volume = {217}, series = {Proceedings of Machine Learning Research}, month = {10--13 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v217/groz23a/groz23a.pdf}, url = {https://proceedings.mlr.press/v217/groz23a.html}, 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.} }
Endnote
%0 Conference Paper %T Active Inference of Extended Finite State Models of Software Systems %A Roland Groz %A Catherine Oriat %A Germán Vega %A Adenilso Simao %A Michael Foster %A Neil Walkinshaw %B Proceedings of 16th edition of the International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2023 %E François Coste %E Faissal Ouardi %E Guillaume Rabusseau %F pmlr-v217-groz23a %I PMLR %P 265--269 %U https://proceedings.mlr.press/v217/groz23a.html %V 217 %X 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.
APA
Groz, R., Oriat, C., Vega, G., Simao, A., Foster, M. & Walkinshaw, N.. (2023). Active Inference of Extended Finite State Models of Software Systems. Proceedings of 16th edition of the International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 217:265-269 Available from https://proceedings.mlr.press/v217/groz23a.html.

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