Generative versus interpretive model-driven development: Moving past ‘It depends’

Abstract

Model-driven development practices are used to improve software quality and developer productivity. However, the design and implementation of an environment with which software can be produced from models is not an easy task. One part of such an environment is the model execution approach: how is the model processed and translated into running software? Experts state that code generation and model interpretation are functionally equivalent. However, a survey that we conducted among several organizations shows that there is a lack of knowledge and guidance in designing the execution approach. In this article we present the results of a literature study on the advantages of both interpretation and generation. We also show, using a case study, how these results can be utilized in the design decisions. Finally, a decision support framework is proposed that can provide the guidance and knowledge for the development of a model-driven engineering environment.