A Survey on the Relevance of the Performance of Model Transformations: Data of the Participant Search and the Questionnaire
Raffaela Groner, Katharina Juhnke, Stefan Götz, Matthias Tichy, Steffen Becker, Vijayshree Vijayshree, Sebastian Frank · OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) · 2021
There are a number of techniques for analyzing and improving the performance of programs written in a general-purpose language. For model transformation languages such techniques are still unknown. These are domain-specific languages, which, simply said, are used to update models or create new models. Thus, these languages realize important operations in the context of Model-Driven Development. Current research about the performance of transformations is strongly focused on the transformation engine, which executes the transformations. Consequently, we conducted an online survey to examine whether techniques for analyzing and improving the performance of transformations are needed. Overall, our data set consists the results of the online survey and the information necessary to repeat the survey. The questionnaire was fully answered by 84 participants. Our data set includes the processed answers and the anonymized raw data. Additionally, we have performed hypothesis tests. Their results and the variables used for them are also part of our data set. In order to support the repeatability of our study, our data set contains not only our questionnaire, but also the results of the snowballing we used for the design of the questions Q9 and Q15. Furthermore, we have conducted a Systematic Literature Review (SLR) about the transformation languages Atlas Transformation Language (ATL), Henshin, QVTo and Viatra, to find suitable participants for our study. Therefore, our data set also contains information about the execution of this SLR and its results.