Measuring Models

@INCOLLECTION{monperrus08b,
  author = {Martin Monperrus and Jean-Marc J{\'e}z{\'e}quel and Jo{\"e}l 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. We show that some of
	these metrics are supported by models. 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, called $\sigma$,
	that is generic over metamodels and allows the easy specification
	of an open-ended wide range of model metrics.},
  doi = {10.4018/978-1-60566-006-6.ch007},
  isbn = {978-1-60566-006-6},
  url = {http://www.monperrus.net/martin/Measuring-models-in-Model-Driven-Software-Development-Integrating-Quality-Assurance.pdf},
  x-abbrv = {chapter}
}