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Heterogeneous megamodel management using collection operators

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Abstract

Model management techniques help tame the complexity caused by the many models used in large-scale software development; however, these techniques have focused on operators to manipulate individual models rather than entire collections of them. In this work, we begin to address this gap by adapting the widely used map, reduce and filter collection operators for collections of models represented by megamodels. Key parts of this adaptation include the special handling of relationships between models and the use of polymorphism to support heterogeneous model collections. We evaluate the complexity of our operators analytically and demonstrate their applicability on six diverse megamodel management scenarios. We describe our tool support for the approach and evaluate its scalability experimentally as well as its applicability on a practical application from the automotive domain.

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Notes

  1. We assume that \({\mathtt {Match}}\) is a commutative model management operator; thus, the operator application on either binding will yield the same output relationship.

  2. In this paper, we use bold font for the megamodel operators \({\mathbf {union}}\), \({\mathbf {map}}\), \({\mathbf {reduce}}\) and \({\mathbf {filter}}\) defined in this paper and italics for all other operators.

  3. Available at http://github.com/adisandro/MMINT.

  4. Detailed instructions to reproduce experiments are available at https://github.com/adisandro/MMINT/wiki/Publications:-SoSyM19.

  5. See [25] for issues concerning handling added elements.

  6. See [15] for the set of rules used for safety case slicing.

  7. The transformation \({\mathtt {SliceCriterionDecorate}}\) (See the signature in Fig. 36a) is first used to add metadata to the deleted elements to support tooling for queries over the results of CIA. We omit further discussion of this because it is outside the scope of the conceptual workflow.

  8. Maudeling: http://atenea.lcc.uma.es/index.php/Main_Page/Resources/Maudeling.

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Acknowledgements

This work is funded by NSERC in collaboration with General Motors.

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Authors and Affiliations

  1. University of Toronto, Toronto, Canada

    Rick Salay, Alessio Di Sandro, Nick L. S. Fung & Marsha Chechik

  2. McMaster University, Hamilton, Canada

    Sahar Kokaly

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  1. Rick Salay
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  4. Nick L. S. Fung
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Correspondence to Rick Salay.

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Communicated by Dr Benoit Combemale.

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Salay, R., Kokaly, S., Di Sandro, A. et al. Heterogeneous megamodel management using collection operators. Softw Syst Model 19, 231–260 (2020). https://doi.org/10.1007/s10270-019-00738-9

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