User-Driven Adaptation of Model Differencing Results

In model-based software development, models are core development artifacts which are typically created and modified by multiple developers over a period of time. In order to be able to reason about the evolution of models, the computation of the differences between different versions of a model, called model differencing, is a crucial activity. However, in general a completely automatic approach to model differencing cannot infer the differences correctly in all cases. Errors in the reported model differences are particularly problematic, when the model differences are used in an automated process as a basis to perform other activities. In this paper, we propose an approach to model differencing in which users can integrate knowledge of how specific model elements changed from one model (version) to the other. By means of this, users can influence the reported model differences to avoid that wrong model differences are reported.