Measuring Models (bibtex)
by Martin Monperrus, Jean-Marc Jézéquel, Joël Champeau, Brigitte Hoeltzener
Abstract:
Model-Driven Engineering (MDE) is an approach to software development that uses models as primary artifacts, from which code, documentation and tests are derived. One way of assessing quality assurance in a given domain is to define domain metrics. As text documents, models can be considered from a syntactic point of view i.e., thought of as graphs. We can readily apply graph-based metrics to them, such as the number of nodes, the number of edges or the fan-in/fan-out distributions. However, these metrics cannot leverage the semantic structuring enforced by each specific metamodel to give domain specific information. Contrary to graph-based metrics, more specific metrics do exist for given domains (such as LOC for programs), but they lack genericity. Our contribution is to propose one metric that is generic over the domains and the metamodels and allows the easy specification of an open-ended wide range of model metrics.
Reference:
Measuring Models (Martin Monperrus, Jean-Marc Jézéquel, Joël Champeau, Brigitte Hoeltzener), Chapter in Model-Driven Software Development: Integrating Quality Assurance (Jörg Rech, Christian Bunse, eds.), IDEA Group, 2008.
Bibtex Entry:
@INCOLLECTION{monperrus08b,
  author = {Martin Monperrus and Jean-Marc Jézéquel and Joël Champeau and Brigitte
	Hoeltzener},
  title = {Measuring Models},
  booktitle = {Model-Driven Software Development: Integrating Quality Assurance},
  publisher = {IDEA Group},
  year = {2008},
  editor = {Jörg Rech and Christian Bunse},
  abstract = {Model-Driven Engineering (MDE) is an approach to software development
	that uses models as primary artifacts, from which code, documentation
	and tests are derived. One way of assessing quality assurance in
	a given domain is to define domain metrics. As text documents, models
	can be considered from a syntactic point of view i.e., thought of
	as graphs. We can readily apply graph-based metrics to them, such
	as the number of nodes, the number of edges or the fan-in/fan-out
	distributions. However, these metrics cannot leverage the semantic
	structuring enforced by each specific metamodel to give domain specific
	information. Contrary to graph-based metrics, more specific metrics
	do exist for given domains (such as LOC for programs), but they lack
	genericity. Our contribution is to propose one metric that is generic
	over the domains and the metamodels and allows the easy specification
	of an open-ended wide range of model metrics.},
  isbn = {978-1-60566-006-6},
  url = {http://www.monperrus.net/martin/Measuring-models-in-Model-Driven-Software-Development-Integrating-Quality-Assurance.pdf}
}
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