Lic abstract - Adrian Pop

Highly integrated domain-specific environments are essential for the
efficient design of complex physical products. However, developing
such design environments is today a resource-consuming error-prone
process that is largely manual. Meta-modeling and meta-programming
are the key to the efficient development of such environments.

The ultimate goal of our research is the development of a meta-modeling
approach and its associated metaprogramming methods for the synthesis of
model-driven product design environments that support modeling and
simulation. Such environments include model-editors, compilers, debuggers
and simulators.  This thesis presents several contributions towards this
vision, in the context of the Modelica framework.

Thus, we have first designed a meta-model for the object-oriented
declarative modeling language Modelica, which facilitates the development
of tools for analysis, checking, querying, documentation, transformation
and management of Modelica models. We have used XML Schema for the
representation of the meta-model, namely, ModelicaXML. Next, we have
focused on the automatic composition, refactoring and transformation of
Modelica models. We have extended the invasive composition environment
COMPOST to handle Modelica models described using ModelicaXML.

The Modelica language semantics has already been specified in the
Relational Meta-Language (RML), which is an executable meta-programming
system based on the Natural Semantics formalism. Using such a metaprogramming
approach to manipulate ModelicaXML, it is possible to automatically synthesize
a Modelica compiler. However, such a task is difficult without the support
for debugging. To address this issue we have developed a debugging framework
for RML, based on abstract syntax tree instrumentation in the RML
compiler and support of efficient tools for complex data structures and
proof-trees visualization.

Our contributions have been implemented within OpenModelica, an open-source
Modelica framework. The evaluations performed using several case studies
show the efficiency of our meta-modeling tools and methods.
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