Model Transformations: State of Practice Study
Model-Driven Software Engineering advocates the use of models in order to abstract from implementation details. Model transformations are a key technology as they enable transformation models to (1) other models, e.g. to add implementation details about a specific platform or to synchronize models for different viewpoints of a system, and to (2) text, e.g., for code generation on the specific platform in the required programming language.
Many different model transformation approaches have been developed in academia and industry and are used by academics and practitioners in diverse application areas, e.g., performance prediction of cloud systems, satellite control procedures, specifying CNC operations.
Typically, a specific model transformation language is selected by a developer mostly due to personal preferences or even due to him/her being the developer of the model transformation language. There does not exist any empirical study on how model transformations are used in practice and what advantages and disadvantages different model transformations approaches have practice. Neither exist a framework to select a model transformation language for a given problem.
The goal of this thesis is to empirically study the state of practice in model transformations. The thesis will contain two major steps. First, a qualitive study using semi-structured interviews will be performed. The interviews will be analyzed using codes and patterns will be identified. Subsequently, quantitative data will be collected in an online survey based on the qualitative codes and patterns. Finally, the data will be analyzed using different methods, e.g., theory generation, Cross-Case Analysis, descriptive, and inferential statistics.
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