Dedicated model transformation languages are claimed to provide many benefits over the use of general purpose languages for developing model transformations. However, the actual advantages associated with the use of MTLs are poorly understood empirically. There is little knowledge and empirical assessment about what advantages and disadvantages hold and where they originate from. In a prior interview study, we elicited expert opinions on what advantages result from what factors and a number of factors that moderate the influence. We aim to quantitatively asses the interview results to confirm or reject the effects posed by different factors. We intend to gain insights into how valuable different factors are so that future studies can draw on these data for designing targeted and relevant studies. We gather data on the factors and quality attributes using an online survey. To analyse the data, we use universal structure modelling based on a structure model. We use significance values and path coefficients produced bz USM for each hypothesised interdependence to confirm or reject correlation and to weigh the strength of influence present. We analyzed 113 responses. The results show that the Tracing and Reuse Mechanisms are most important overall. Though the observed effects were generally 10 times lower than anticipated. Additionally, we found that a more nuanced view of moderation effects is warranted. Their moderating influence differed significantly between the different influences, with the strongest effects being 1000 times higher than the weakest. The empirical assessment of MTLs is a complex topic that cannot be solved by looking at a single stand-alone factor. Our results provide clear indication that evaluation should consider transformations of different sizes and use-cases. Language development should focus on providing transformation specific reuse mechanisms .

翻译：专用模型转换语言被声称在开发模型转换时比通用语言具有诸多优势。然而，关于使用MTL的实际优势缺乏实证理解。对于这些优势与劣势的具体表现及成因，目前认知不足且缺乏实证评估。在前期访谈研究中，我们收集了专家关于不同因素如何产生优势以及若干调节因素的意见。本研究旨在定量评估访谈结果，以确认或否定不同因素提出的效应。我们试图深入了解各因素的价值程度，以便未来研究可以基于这些数据设计有针对性的相关研究。我们通过在线调查收集因素及质量属性数据。数据分析采用基于结构化模型的通用结构建模方法。利用USM产生的显著值和路径系数，对每项假设的相互依赖性进行确认或否定相关性，并衡量影响强度。我们分析了113份回复。结果表明，整体而言，溯源与复用机制最为重要，尽管观测到的效应普遍比预期低10倍。此外，我们发现有必要以更细致的视角看待调节效应。不同影响之间的调节作用差异显著，最强效应比最弱效应高1000倍。MTL的实证评估是一个复杂课题，不能仅通过单一因素解决。我们的结果明确表明，评估应考虑不同规模和用途的转换。语言开发应侧重于提供转换专属的复用机制。