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Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models

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Graph Transformation, Specifications, and Nets

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10800))

Abstract

Automated model generation can be highly beneficial for various application scenarios including software tool certification, validation of cyber-physical systems or benchmarking graph databases to avoid tedious manual synthesis of models. In the paper, we present a long-term research challenge how to generate graph models specific to a domain which are consistent, diverse, scalable and realistic at the same time.

We provide foundations for a class of model generators along a refinement relation which operates over partial models with 3-valued representation and ensures that subsequently derived partial models preserve the truth evaluation of well-formedness constraints in the domain. We formally prove completeness, i.e. any finite instance model of a domain can be generated by model generator transformations in finite steps and soundness, i.e. any instance model retrieved as a solution satisfies all well-formedness constraints. An experimental evaluation is carried out in the context of a statechart modeling tool to evaluate the trade-off between different characteristics of model generators.

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Notes

  1. 1.

    The authors’ copy of this paper is available at https://inf.mit.bme.hu/research/publications/towards-model-generation together with the proofs of theorems presented in Sect. 4.

  2. 2.

    CPU: Intel Core-i5-m310M, MEM: 16 GB, OS: Windows 10 Pro.

  3. 3.

    Due to the excessive amount of homework models, we took a uniform random sample of 100 models from that model set.

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Acknowledgements

The authors are really grateful for the anonymous reviewers and Szilvia Varró-Gyapay for the numerous constructive feedback to improve the current paper. This paper is partially supported by MTA-BME Lendület Research Group on Cyber-Physical Systems, and NSERC RGPIN-04573-16 project.

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Authors and Affiliations

  1. Budapest University of Technology and Economics, Budapest, Hungary

    Dániel Varró, Oszkár Semeráth, Gábor Szárnyas & Ákos Horváth

  2. MTA-BME Lendület Research Group on Cyber-Physical Systems, Budapest, Hungary

    Dániel Varró, Oszkár Semeráth & Gábor Szárnyas

  3. Department of Electrical and Computer Engineering, McGill University, Montreal, Canada

    Dániel Varró

  4. IncQuery Labs Ltd., Budapest, Hungary

    Ákos Horváth

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  1. Dániel Varró
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  2. Oszkár Semeráth
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  3. Gábor Szárnyas
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  4. Ákos Horváth
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Correspondence to Oszkár Semeráth .

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  1. Department of Informatics, University of Leicester, Leicester, United Kingdom

    Reiko Heckel

  2. Fachbereich Mathematik und Informatik, Philipps-Universität Marburg, Marburg, Germany

    Gabriele Taentzer

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Varró, D., Semeráth, O., Szárnyas, G., Horváth, Á. (2018). Towards the Automated Generation of Consistent, Diverse, Scalable and Realistic Graph Models. In: Heckel, R., Taentzer, G. (eds) Graph Transformation, Specifications, and Nets. Lecture Notes in Computer Science(), vol 10800. Springer, Cham. https://doi.org/10.1007/978-3-319-75396-6_16

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  • DOI: https://doi.org/10.1007/978-3-319-75396-6_16

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