The FieSta Framework: Fine-grained Derivation and Configuration - Model-Driven and Software Product Line Engineering - page 157

we cannot guarantee that the specific rules (advices) will be
executed with the suitable parameters.
5.5. Summary
In this chapter, we first introduced the concept and
the need for fine-grained variation and configuration. Fine-
grained variation arises when we need to define variable
artifacts of the same kind. Coarse-grained approaches do not
allow us to configure differently the instances of the same
metamodel. Introducing new models making some variations
explicit could be possible, but it is not a general solution.
To solve this issue, we introduce binding and constraint
models. On the one hand, binding models allow us to capture
the links between a model and a variability model, thus
enabling the fine-grained configuration of model elements. On
the other hand, the constraint model specifies precisely the
semantics of the bindings using cardinality and structural
dependency properties. This specification is based mainly on
OCL sentences, which are common and supported by various
validation tools. We have also described the metamodels we
created to support the creation of constraint, binding, and
decision models. Our basic idea to obtain a final execution
scheduling was to construct a baseline scheduling, which
is modified according to valid feature configurations. We
tune our general strategy for validating binding models
against constraint models and for generating executable
modeltransformationworkflowsfromdecisionmodels.Binding
models imply modification of the baseline scheduling taking
into account not only features from feature configurations,
but also bindings from binding models. Aspects responsible
for the rules scheduling associated with variations, query the
binding model to get the precise element impacted by the
rule. The decision metamodel has been extended to allow
for the derivation of products taking into account binding
models. Finally, we presented the limitations of our approach
for deriving products based on decision models.