Hebig, ReginaSeidl, ChristophBerger, ThorstenPedersen, John KookWasowski, AndrzejBecker, SteffenBogicevic, IvanHerzwurm, GeorgWagner, Stefan2019-03-142019-03-142019978-3-88579-686-2https://dl.gi.de/handle/20.500.12116/20884In Model-Driven Software Development, models are processed automatically to support the creation, build, and execution of systems. A large variety of dedicated model-transformation languages exists, promising to efficiently realize the automated processing of models. To investigate the actual benefit of using such specialized languages, we performed a large-scale controlled experiment in which 78 subjects solved 231 individual tasks using three languages. The experiment sheds light on commonalities and differences between model transformation languages (ATL, QVT-O) and on benefits of using them in common development tasks (comprehension, change, and creation) against a modern general-purpose language (Xtend). The results of our experiment show no statistically significant benefit of using a dedicated transformation language over a modern general-purpose language. However, we were able to identify several aspects of transformation programming where domain-specific transformation languages do appear to help, including copying objects, context identification, and conditioning the computation on types.enModel Transformation LanguagesExperimentXtendATLQVTText/Conference Paper10.18420/se2019-251617-5468