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Measuring Models

Measuring Models

Martin Monperrus, Jean-Marc Jézéquel, Joël Champeau, Brigitte Hoeltzener
Copyright: © 2009 |Pages: 23
ISBN13: 9781605660066|ISBN10: 160566006X|EISBN13: 9781605660073
DOI: 10.4018/978-1-60566-006-6.ch007
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MLA

Monperrus, Martin, et al. "Measuring Models." Model-Driven Software Development: Integrating Quality Assurance, edited by Jörg Rech and Christian Bunse, IGI Global, 2009, pp. 147-169. https://doi.org/10.4018/978-1-60566-006-6.ch007

APA

Monperrus, M., Jézéquel, J., Champeau, J., & Hoeltzener, B. (2009). Measuring Models. In J. Rech & C. Bunse (Eds.), Model-Driven Software Development: Integrating Quality Assurance (pp. 147-169). IGI Global. https://doi.org/10.4018/978-1-60566-006-6.ch007

Chicago

Monperrus, Martin, et al. "Measuring Models." In Model-Driven Software Development: Integrating Quality Assurance, edited by Jörg Rech and Christian Bunse, 147-169. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-006-6.ch007

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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 s, that is generic over metamodels and allows the easy specification of an open-ended wide range of model metrics.

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