Model-driven engineering (MDE) promotes the use of abstractions to simplify the development of complex software systems. Through several model management tasks (e.g., model verification, re-factoring, model transformation), many software development tasks can be automated. For example, model-to-text transformations (M2T) are used to realize textual development artefacts (e.g., documentation, configuration scripts, code, etc.) from underlying source models. Despite the importance of M2T transformation, contemporary M2T languages lack support for developing transformations that scale. As MDE is applied to systems of increasing size and complexity, a lack of scalable M2T transformations and other model management tasks hinders industrial adoption. This is largely due to the fact that model management tools do not support efficient propagation of changes from models to other development artefacts. As such, the re-synchronisation of generated textual artefacts with underlying system models can take considerably large amount of time to execute due to redundant re-computations. This thesis investigates scalability in the context of M2T transformation, and proposes two novel techniques that enable efficient incremental change propagation from models to generated textual artefacts. In contrast to existing incremental M2T transformation technique, which relies on model differencing, our techniques employ fundamentally different approaches to incremental change propagation: they use a form of runtime analysis that identifies the impact of source model changes on generated textual artefacts. The structures produced by this runtime analysis, are used to perform efficient incremental transformations (scalable transformations). This claim is supported by the results of empirical evaluation which shows that the techniques proposed in this thesis can be used to attain an average reduction of 60% in transformation execution time compared to non-incremental (batch) transformation.