Background: 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 and disadvantages associated with the use of model transformation languages are poorly understood empirically. There is little knowledge over what advantages and disadvantages hold in which cases and where they originate from. In a prior interview study, we elicited expert opinions on what advantages result from what factors surrounding model transformation languages as well as a number of moderating factors that moderate the influence. Objective: We aim to quantitatively asses the interview results to confirm or reject the influences and moderation effects posed by different factors and to gain insights into how valuable different factors are to the discussion. Method: We gather data on the factors and quality attributes using an online survey. To analyse the data and examine the hypothesised influences and moderations we use universal structure modelling based on a structural equation model. Universal structure modelling will produce significance values and path coefficients for each hypothesised and modelled interdependence between factors and quality attributes that can be used to confirm or reject correlation and to weigh the strength of influence present. Limitations: Due to the complexity and abstractness of the concepts under investigation, a measurement via reflective or formative indicators is not possible. Instead participants are queried about their assessment of concepts through a single item question. We further assume that positive and negative effects of a feature are more prominent if the feature is used more frequently.
翻译:在先前的访谈研究中,我们征求专家意见,了解围绕模式转换语言的各种因素以及一些调节因素产生哪些好处,从而减轻影响。目标:我们的目标是对采访结果进行定量评估,以确认或否定不同因素的影响和温和效应,并深入了解不同因素对讨论的价值。方法:我们通过在线调查收集关于各种因素和质量属性的数据。分析数据并审查基于结构等式模型的假设影响和温和因素。通用结构建模将产生重要价值和路径系数,说明每一种假设和模拟因素与质量属性之间的相互依存关系,用以确认或否定相关性,衡量现有影响的力量。我们的目标是量化评估结果,以确认或否定不同因素的影响和温和效应,并深入了解不同因素对讨论的价值。方法:我们利用在线调查来收集关于各种因素和质量属性的数据。分析数据并审查我们基于结构等式模型的假设影响和温和因素。通用结构建模将产生重要价值和路径系数,用以确认或否定相关性,衡量现有影响的力量。如果通过更突出的特征指标衡量,则可能采用更突出的特征指标。