An Exploratory Study on Performance Engineering in Model Transformations

Abstract

Model-Driven Software Engineering is used to deal with the increasing complexity of software, but this trend also leads to larger and more complex models and model transformations. While improving the performance of transformation engines has been a focus, there does not exist any empirical study on how transformation developers deal with performance issues. We used a quantitative questionnaire to investigate whether the performance of transformations is actually important for transformation developers. Based on the answers to the questionnaire, we conducted qualitative semi-structured interviews. The results of the online survey show that 43 of 81 participants have already tried to improve the performance of a transformation and 34 participants are sometimes or only rarely satisfied with the execution performance. Based on the answers from our 13 interviews, we identified different strategies to prevent or find performance issues in model transformations as well as different types of causes of performance issues and solutions to resolve them. We compiled a collection of tool features perceived helpful by the interviewees for finding causes. Overall, our results show that performance of transformations is relevant and that there is a lack of support for transformation developers without detailed knowledge of the engine to solve performance issues. This summary refers to our work, which was accepted for the Foundation Track of the ACM / IEEE 23rd International Conference on Model Driven Engineering Languages and Systems (MODELS) in 2020.