Advantages and Disadvantages of (Dedicated) Model Transformation Languages A Qualitative Interview Study

In a recent study we have shown, that a large number of claims about model transformation languages have not yet been substantiated and are made without much context to be able to critically asses their merit or built meaningful empirical studies around them. The objective of our work was to elicit the reasoning, influences and background knowledge of researchers and practitioners that lead them to assuming benefits or drawbacks of model transformation languages compared to general purpose languages for the task of developing model transformations. For this we put our focus on the following 6 properties that have strong relevance for wider adoption: Ease of writing, Comprehensibility, Tool Support, Practical Expressiveness, Productivity, Reuse and Maintainability. We conducted a large-scale interview study involving 56 participants from research and industry. Interviewees were presented with claims about model transformation languages and were asked to provide reasons as to why they believe or dispute these claims. Our interviews show, that the general purpose expressiveness of GPLs, the domain specific capabilities of MTLs and the tooling of MTLs all have strong influences on how people view properties of model transformation languages. Their specific influences differ depending on different concrete characteristics, such as, for example, Bidirectionality or Debugging Tooling. Moreover, the choice of MTL, the use case for which a transformation should be developed as well as the skills of involved stakeholders have an indirect effect on MTL properties by changing the contextual circumstances under examination. We conclude that there is a broad body of experience of interviews that suggests positive and negative influences for properties of MTLs. However, our qualitative data suggests that much needs to be done in order to convey the viability of model transformation languages.