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FSE 2018

Model transformation languages under a magnifying glass: a controlled experiment with Xtend, ATL, and QVT

Regina Hebig, Christoph Seidl, Thorsten Berger, John Kook Pedersen, Andrzej Wasowski

Full textDOI 10.1145/3236024.3236046dblp

Abstract

In Model-Driven Software Development, models are automatically processed 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 over 78 subjects solve 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). Our results 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.

BibTeX
@inproceedings{Hebig-al:FSE18,
  author    = {Regina Hebig and
               Christoph Seidl and
               Thorsten Berger and
               John Kook Pedersen and
               Andrzej Wasowski},
  title     = {Model transformation languages under a magnifying glass: a controlled experiment with Xtend, {ATL,} and {QVT}},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {445--455},
  publisher = {{ACM}},
  year      = {2018},
}

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