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Generative software development

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 May 2010Embargo end date: 01 Jan 2014Publisher:ACMJournal:Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2

Authors: Steven Völkel; Bernhard Rumpe; Ingo Weisemöller; Martin Schindler;

doi: 10.1145/1810295.1810436 , 10.48550/arxiv.1409.2368

arXiv: http://arxiv.org/abs/1409.2368

Generative software development

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Abstract

Generation of software from modeling languages such as UML and domain specific languages (DSLs) has become an important paradigm in software engineering. In this contribution, we present some positions on software development in a model based, generative manner based on home grown DSLs as well as the UML. This includes development of DSLs as well as development of models in these languages in order to generate executable code, test cases or models in different languages. Development of formal DSLs contains concepts of meta-models or grammars (syntax), context conditions (static analysis and quality assurance) as well as possibilities to define the semantics of a language. The growing number and complexity of DSLs is addressed by concepts for the modular and compositional development of languages and their tools. Moreover, we introduce approaches to code generation and model transformation. Finally, we give an overview of the relevance of DSLs for various steps of software development processes.

2 pages, 8 figures

Related Organizations

RWTH Aachen University
Germany

Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average