'abstract' => 'We introduce branch-and-infer, a unifying framework for integer linear programming and finite domain constraint programming. We use this framework to compare the two approaches with respect to their modeling and solving capabilities, to introduce symbolic constraint abstractions into integer programming, and to discuss possible combinations of the two approaches.',
'abstract' => 'We introduce branch-and-infer, a unifying
framework for integer linear programming and finite domain
constraint programming. We use this framework to compare the
two approaches with respect to their modeling and solving
capabilities, to introduce symbolic constraint abstractions
into integer programming, and to discuss possible combinations
'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.',
'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.',
'author' => 'Martin Monperrus and Jean-Marc Jézéquel and Joël Champeau and Brigitte Hoeltzener',
'author' => 'Martin Monperrus and Jean-Marc Jézéquel and Joël Champeau and Brigitte Hoeltzener',
Martin Monperrus
15 years ago