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Architecture Derivation in Product Line Development Through Model Transformations

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Abstract

Product architecture derivation is a crucial activity in Software Product Line (SPL) development since any inadequate decisions made during the architecture design directly impact on the non-functional properties of the product under development. Although some methods for architecture derivation have been proposed in the last few years, there is still a need for approaches that model the impact among architectural design decisions and quality attributes and use this information to drive the derivation of high-quality product architectures. This paper, presents a set of guidelines for the definition of pattern-based quality-driven architectural transformations in a Model-Driven SPL development environment. These guidelines rely both on a multimodel that represents the product line from multiple viewpoints as well as on a derivation process that makes use of this multimodel to derive a product architecture that meets the quality requirements. The feasibility of the approach is illustrated using a case study on the automotive domain.

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Notes

  1. 1.

    A quality attribute level indicates a threshold at which a system must perform to be useful to the stakeholders [20].

  2. 2.

    The proxy metaclasses follow the naming convention E+ClassName, where ClassName is the base class that is extended.

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Acknowledgements

This research is supported by the MULTIPLE project (MICINN TIN2009-13838) and the ValI+D program (ACIF/201 1/235).

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Correspondence to Javier González-Huerta .

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González-Huerta, J., Insfran, E., Abrahão, S., McGregor, J.D. (2014). Architecture Derivation in Product Line Development Through Model Transformations. In: José Escalona, M., Aragón, G., Linger, H., Lang, M., Barry, C., Schneider, C. (eds) Information System Development. Springer, Cham. https://doi.org/10.1007/978-3-319-07215-9_30

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  • DOI: https://doi.org/10.1007/978-3-319-07215-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07214-2

  • Online ISBN: 978-3-319-07215-9

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