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Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report

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Integrated Formal Methods (IFM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9681))

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Abstract

Many companies struggle with large amounts of legacy software that is difficult to maintain and to extend. Refactoring legacy code typically requires large efforts and introduces serious risks because often crucial business assets are hidden in legacy components. We investigate the support of formal techniques for the rejuvenation of legacy embedded software, concentrating on control components. Model learning and equivalence checking are used to improve a new implementation of a legacy control component. Model learning is applied to both the old and the new implementation. The resulting models are compared using an equivalence check of a model checker. We report about our experiences with this approach at Philips. By gradually increasing the set of input stimuli, we obtained implementations of a power control service for which the learned behaviour is equivalent.

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Notes

  1. 1.

    www.semanticdesigns.com.

  2. 2.

    www.graphviz.org/.

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Acknowledgements

We are most grateful to Joshua Moerman for helping with the experiments with the TTT algorithm. We also thank Petra van den Bos for careful proofreading of an earlier version. This research was supported by STW project 11763 (ITALIA) and the Dutch national program COMMIT.

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Correspondence to Mathijs Schuts .

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Schuts, M., Hooman, J., Vaandrager, F. (2016). Refactoring of Legacy Software Using Model Learning and Equivalence Checking: An Industrial Experience Report. In: Ábrahám, E., Huisman, M. (eds) Integrated Formal Methods. IFM 2016. Lecture Notes in Computer Science(), vol 9681. Springer, Cham. https://doi.org/10.1007/978-3-319-33693-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-33693-0_20

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