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tutorial

Describing variability with domain-specific languages and models

Published:06 September 2021Publication History

ABSTRACT

This tutorial will teach participants about domain-specific languages and models, where they can best be used (and where not), and how to apply them effectively to improve the speed and quality of product development within a product line.

References

  1. Czarnecki, K., Eisenecker, U., Generative Programming, Methods, Tools, and Applications, Addison-Wesley, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Kelly, S., Tolvanen, J.-P., Domain-Specific Modeling: Enabling Full Code Generation, Wiley-IEEE Computer Society Press, 2008 Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Tolvanen, J.-P., Kelly, S., Defining Domain-Specific Modeling Languages to Automate Product Derivation: Collected Experiences. Proceedings of the 9th International Software Product Line Conference, H. Obbink, K. Pohl, Springer-Verlag, LNCS 3714, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Tolvanen, J.-P., Kelly, S., How Domain-Specific Modeling Languages Address Variability in Product Line Development: Investigation of 23 cases. Proceedings of 23rd International Systems and Software Product Line Conference (SPLC19). ACM, 2019. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Tolvanen, J-P. and Kelly, S. Model-Driven Development Challenges and Solutions --- Experiences with Domain-Specific Modelling in Industry. In Proceedings of the 4th International Conference on Model-Driven Engineering and Software Development, SCITEPRESS Science and Technology Publications, Lda, 2016Google ScholarGoogle ScholarCross RefCross Ref

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  1. Describing variability with domain-specific languages and models

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        • Published in

          cover image ACM Conferences
          SPLC '21: Proceedings of the 25th ACM International Systems and Software Product Line Conference - Volume A
          September 2021
          239 pages
          ISBN:9781450384698
          DOI:10.1145/3461001

          Copyright © 2021 ACM

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 6 September 2021

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          Overall Acceptance Rate167of463submissions,36%
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