Main Article Content
Model merging addresses the problem of combining information from a set of models into a single one. This task is considered to be an important step in various software engineering practices. When many (more than two) models need to be merged, the most practiced technique is to perform the merge in a pairwise way, without considering the order of merging. The problem with this technique is that the resulting quality is not guaranteed because it is influenced by such an order. In this paper, we propose a pairwise approach for model merging aiming to provide better results by taking into account the order of merging. This approach proposes to combine models in an iterative process until obtaining only one model. At each iteration, we first compare each pair of models in order to measure the similarity between them and to identify the correspondences between their component elements. This is performed using two heuristic-based operators respectively named compare and match. After that, we identify the most similar pairs of models and merge them using a proposed operator. We have implemented our approach as tool support called 3M and evaluated it on a set of case studies.