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Automated Backend Selection for ProB Using Deep Learning

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NASA Formal Methods (NFM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11460))

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Abstract

Employing formal methods for software development usually involves using a multitude of tools such as model checkers and provers. Most of them again feature different backends and configuration options. Selecting an appropriate configuration for a successful employment becomes increasingly hard. In this article, we use machine learning methods to automate the backend selection for the ProB model checker. In particular, we explore different approaches to deep learning and outline how we apply them to find a suitable backend for given input constraints.

Computational support and infrastructure was provided by the “Centre for Information and Media Technology” (ZIM) at the University of Düsseldorf (Germany).

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Notes

  1. 1.

    www3.hhu.de/stups/downloads/prob/source/ProB_public_examples.tgz.

  2. 2.

    https://github.com/hhu-stups/prob-examples-metadata.

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Author information

Authors and Affiliations

  1. Heinrich-Heine-University, Düsseldorf, Germany

    Jannik Dunkelau, Sebastian Krings & Joshua Schmidt

  2. Niederrhein University of Applied Sciences, Mönchengladbach, Germany

    Sebastian Krings

Authors
  1. Jannik Dunkelau
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  2. Sebastian Krings
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  3. Joshua Schmidt
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Correspondence to Jannik Dunkelau .

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Editors and Affiliations

  1. NASA, Houston, TX, USA

    Julia M. Badger

  2. Iowa State University, Ames, IA, USA

    Kristin Yvonne Rozier

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Dunkelau, J., Krings, S., Schmidt, J. (2019). Automated Backend Selection for ProB Using Deep Learning. In: Badger, J., Rozier, K. (eds) NASA Formal Methods. NFM 2019. Lecture Notes in Computer Science(), vol 11460. Springer, Cham. https://doi.org/10.1007/978-3-030-20652-9_9

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  • DOI: https://doi.org/10.1007/978-3-030-20652-9_9

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