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Reverse-Engineering EFSMs with Data Dependencies

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Book cover Testing Software and Systems (ICTSS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13045))

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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.

Michael Foster and Neil Walkinshaw are funded by the EPSRC CITCoM project.

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Notes

  1. 1.

    The inference tool we use here [18] currently supports only \(+\), −, and \(\times \) for integers, and literal assignment for strings, although our GP has broader support [16, 37].

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Authors and Affiliations

  1. Department of Computer Science, The University of Sheffield, Regent Court, Sheffield, S1 4DP, UK

    Michael Foster, John Derrick & Neil Walkinshaw

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  1. Michael Foster
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  2. John Derrick
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  3. Neil Walkinshaw
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Correspondence to Michael Foster .

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Editors and Affiliations

  1. University College London, London, UK

    David Clark

  2. Middlesex University, London, UK

    Hector Menendez

  3. Telecom SudParis, Evry Cedex, France

    Ana Rosa Cavalli

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Foster, M., Derrick, J., Walkinshaw, N. (2022). Reverse-Engineering EFSMs with Data Dependencies. In: Clark, D., Menendez, H., Cavalli, A.R. (eds) Testing Software and Systems. ICTSS 2021. Lecture Notes in Computer Science, vol 13045. Springer, Cham. https://doi.org/10.1007/978-3-031-04673-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-04673-5_3

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