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Decision support for choosing architectural assets in the development of software-intensive systems: The GRADE taxonomy

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Published:07 September 2015Publication History

ABSTRACT

Engineering software-intensive systems is a complex process that typically involves making many critical decisions. A continuous challenge during system design, analysis and development is deciding on the reference architecture that could reduce risks and deliver the expected functionality and quality of a product or a service to its users. The lack of evidence in documenting strategies supporting decision-making in the selection of architectural assets in systems and software engineering creates an impediment in learning, improving and also reducing the risks involved. In order to fill this gap, ten experienced researchers in the field of decision support for the selection of architectural assets in engineering software-intensive systems conducted a workshop to reduce traceability of strategies and define a dedicated taxonomy. The result was the GRADE taxonomy, whose key elements can be used to support decision-making as exemplified through a real case instantiation for validation purposes. The overall aim is to support future work of researchers and practitioners in decision-making in the context of architectural assets in the development of software-intensive systems. The taxonomy may be used in three ways: (i) identify new opportunities in structuring decisions; (ii) support the review of alternatives and enable informed decisions; and (iii) evaluate decisions by describing in a retrospective fashion decisions, factors impacting the decision and the outcome.

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      • Published in

        cover image ACM Other conferences
        ECSAW '15: Proceedings of the 2015 European Conference on Software Architecture Workshops
        September 2015
        364 pages
        ISBN:9781450333931
        DOI:10.1145/2797433

        Copyright © 2015 ACM

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        Publication History

        • Published: 7 September 2015

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        ECSAW '15 Paper Acceptance Rate51of77submissions,66%Overall Acceptance Rate80of120submissions,67%

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