  Blended modeling is an emerging paradigm involving seamless interaction
between multiple notations for the same underlying modeling language. We focus
on a model-driven engineering (MDE) approach based on meta-models to develop
textual languages to improve the blended modeling capabilities of modeling
tools. In this thesis, we propose an approach that can support the co-evolution
of meta-models and grammars as language engineers develop textual languages in
a meta-model-based MDE setting. Firstly, we comprehensively report on the
challenges and limitations of modeling tools that support blended modeling, as
well as opportunities to improve them. Second, we demonstrate how language
engineers can extend Xtext's generator capabilities according to their needs.
Third, we propose a semi-automatic method to transform a language with a
generated grammar into a Python-style language. Finally, we provide a solution
(i.e., GrammarOptimizer) that can support rapid prototyping of languages in
different styles and the co-evolution of meta-models and grammars of evolving
languages.
