一句话：大规模对照实验比较模型转换语言 ATL、QVT-O 与通用语言 Xtend，未发现专用语言整体显著优势，但揭示其部分任务优势。
该文关注模型驱动开发中专用模型转换语言是否比现代通用语言更高效。作者开展大规模受控实验，78 余名参与者使用 ATL、QVT-O 和 Xtend 完成 231 个任务，涵盖理解、修改和创建三类常见开发活动。结果表明，与通用语言 Xtend 相比，专用转换语言在整体上没有统计显著收益；不过，在复制对象、识别上下文以及依据类型条件化计算等方面，领域专用转换语言确实表现出帮助。研究还揭示了 ATL 与 QVT-O 的共性与差异。
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.
@inproceedings{paperbot4638,
title = {Model transformation languages under a magnifying glass: a controlled experiment with Xtend, ATL, and QVT},
author = {Regina Hebig and Christoph Seidl and Thorsten Berger and John Kook Pedersen and Andrzej Wąsowski},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3236046}
}