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Efficient model similarity estimation with robust hashing

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Abstract

As model-driven engineering (MDE) is increasingly adopted in complex industrial scenarios, modeling artefacts become a key and strategic asset for companies. As such, any MDE ecosystem must provide mechanisms to protect and exploit them. Current approaches depend on the calculation of the relative similarity among pairs of models. Unfortunately, model similarity calculation mechanisms are computationally expensive which prevents their use in large repositories or very large models. In this sense, this paper explores the adaptation of the robust hashing technique to the MDE domain as an efficient estimation method for model similarity. Indeed, robust hashing algorithms (i.e., hashing algorithms that generate similar outputs from similar input data) have proved useful as a key building block in intellectual property protection, authenticity assessment and fast comparison and retrieval solutions for different application domains. We present a detailed method for the generation of robust hashes for different types of models. Our approach is based on the translation to the MDE domain of diverse techniques such as summary extraction, minhash generation and locality-sensitive hash function families, originally developed for the comparison and classification of large datasets. We validate our approach with a prototype implementation and show that: (1) our approach can deal with any graph-based model representation; (2) a strong correlation exists between the similarity calculated directly on the robust hashes and a distance metric calculated over the original models; and (3) our approach scales well on large models and greatly reduces the time required to find similar models in large repositories.

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Notes

  1. http://web.emn.fr/x-info/atlanmod/index.php?title=Zoos

  2. https://gitlab.com/smartine/RobustModelHashing

  3. note that EMF models may have more than one root or just store the elements at the package level without root (we can then consider all those elements as roots). In that case, root contents are added sequentially to A.

  4. the use of the EMFCompare differences list’s size as metric, requires the use of models of similar size for it to be meaningful.

  5. https://code.google.com/archive/a/eclipselabs.org/p/ecore-mutator

  6. we used Spearman rank correlation obtaining a coefficients ranging from 81 to 90.

  7. Experiments are performed on A Intel® \({\hbox {Core}}^{\hbox {TM}}\) i5-6200U CPU @ 2.30GHz 4 cores, running Ubuntu 16.04

  8. https://github.com/atlanmod/mondo-atlzoo-benchmark/tree/master/fr.inria.atlanmod.instantiator

  9. Hashing may also be used as a key building block in the construction of a watermarking scheme [1] for models but to be most effective the hashing creation process should be different from the one presented here [39].

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Acknowledgements

This work has been partially funded by the Spanish government (LOCOSS project - PID2020-114615RB-I00) and the ECSEL Joint Undertaking (AIDOaRt project - grant agreement No 101007350).

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Authors and Affiliations

  1. IMT Atlantique, Lab-STICC, UMR 6285, Brest, France

    Salvador Martínez

  2. Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France

    Sébastien Gérard

  3. ICREA - UOC, Barcelona, Spain

    Jordi Cabot

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  1. Salvador Martínez
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Correspondence to Salvador Martínez.

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Communicated by Gregor Engels.

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Martínez, S., Gérard, S. & Cabot, J. Efficient model similarity estimation with robust hashing. Softw Syst Model 21, 337–361 (2022). https://doi.org/10.1007/s10270-021-00915-9

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