Advantages and Disadvantages of (Dedicated) Model Transformation Languages A Qualitative Interview Study
This work addresses the need for empirical validation of claims about model transformation languages in model-driven development, which is incremental as it gathers qualitative insights to inform future studies and improvements.
The study tackled the problem of unsubstantiated claims about model transformation languages (MTLs) by conducting a qualitative interview study with 56 participants to elicit reasoning behind perceived benefits and drawbacks. The result identified key factors like general-purpose expressiveness, domain-specific capabilities, tooling, and contextual moderators influencing perceptions, suggesting that more empirical work and improvements are needed to convey MTL viability.
Model driven development envisages the use of model transformations to evolve models. Model transformation languages, developed for this task, are touted with many benefits over general purpose programming languages. However, a large number of these claims have not yet been substantiated. They are also made without the context necessary to be able to critically assess their merit or built meaningful empirical studies around them. The objective of our work is to elicit the reasoning, influences and background knowledge that lead people to assume benefits or drawbacks of model transformation languages. 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 for their assessment thereof. We qualitatively analysed the responses to find factors that influence the properties of model transformation languages as well as explanations as to how exactly they do so. Our interviews show, that general purpose expressiveness of GPLs, domain specific capabilities of MTLs as well as tooling all have strong influences on how people view properties of model transformation languages. Moreover, the Choice of MTL, the Use Case for which a transformation should be developed as well as the Skills of involved stakeholders have a moderating effect on the influences, by changing the context to consider. There is a broad body of experience, that suggests positive and negative influences for properties of MTLs. Our data suggests, that much needs to be done in order to convey the viability of model transformation languages. Efforts to provide more empirical substance need to be undergone and lackluster language capabilities and tooling need to be improved upon. We suggest several approaches for this that can be based on the results of the presented study.