Traceability and reuse mechanisms, the most important properties of model transformation languages

Abstract

Context

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 and even less empirical assessment about 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. We further intend to gain insights into how valuable different factors are to the discussion so that future studies can draw on these data for designing targeted and relevant studies.

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 produces 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.

Results

We analyzed 113 responses. The results show that the MTL capabilities Tracing and Reuse Mechanisms are most important overall. Though the observed effects were generally 10 times lower than anticipated. Furthermore, we found that moderation effects need to be individually assessed for each influence on a quality attribute. The moderation effects of a single moderating variable vary significantly for each influence, with the strongest effects being 1000 times higher than the weakest.

Conclusion

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 that go beyond mapping one elements attributes to another. Language development on the other hand should focus on providing practical, transformation specific reuse mechanisms that allow MTLs to excel in areas such as maintainability and productivity compared to GPLs.