Measuring Differences To Compare Sets Of Models And Improve Diversity In MDE

Owning sets of models is crucial in many fields, so as to validate concepts or to test algorithms that handle models, model transformations. Since such models are not always available, generators can be used to automatically generate sets of models. Unfortunately, the generated models are very close to each others in term of graph structure and element naming is poorly diverse. Usually, they cover very badly the solutions' space. In this paper, we propose novel measures to estimate differences between two models and we provide solutions to handle a whole set of models and perform several operations on its models: comparing them, selecting the most diverse and representative and graphically view the diversity. Implementations presented in this paper are gathered in a tool named COMODI. We applied these model comparison measures in order to improve diversity in MDE using a genetic algorithm.