Abstract
Building models with a domain specific language enables targeting specific platform and framework functionality. We built a domain specific language for use in modeling applications targeting our business application framework. Such models are used for tasks including generating C# code and producing object-relational mappings for business objects. The paper briefly describes the framework and its accompanying domain specific language and then describes issues we encountered in using an unconstrained UML tool to express our models, solutions we developed to deal with those issues and observations about the suitability of UML for application to such problems. We found that making a general-purpose, extensible modeling language serve the needs of a targeted domain specific language is a lot of work and is only partially successful. We conclude that what is needed is a more general purpose framework for creating domain specific languages and tools for them.
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References
Extension Mechanisms Overview, §2.6.1. OMG Unified Modeling Language Specification Version 1.5. Object Management Group (March 2003), http://www.omg.org/cgi-bin/doc?formal/03-03-01
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Profiles, §18.13.5. UML 2.0 Superstructure Specification. Object Management Group (August 2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Anonsen, S. (2005). Experiences in Modeling for a Domain Specific Language. In: Jardim Nunes, N., Selic, B., Rodrigues da Silva, A., Toval Alvarez, A. (eds) UML Modeling Languages and Applications. UML 2004. Lecture Notes in Computer Science, vol 3297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31797-5_19
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DOI: https://doi.org/10.1007/978-3-540-31797-5_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25081-4
Online ISBN: 978-3-540-31797-5
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Extensibility Mechanism
- Object Management Group
- Business Application
- Domain Specific Language
- Code Synchronization
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
