Declarative rules are frequently used in model refactoring in order to detect refactoring opportunities and to apply the appropriate ones. However, a large number of rules is required to obtain a complete specification of refactoring opportunities. Companies usually have accumulated examples of refactorings from past maintenance experiences. Based on these observations, we consider the model refactoring problem as a multi objective problem by suggesting refactoring sequences that aim to maximize both structural and textual similarity between a given model (the model to be refactored) and a set of poorly designed models in the base of examples (models that have undergone some refactorings) and minimize the structural similarity between a given model and a set of well‐designed models in the base of examples (models that do not need any refactoring). To this end, we use the Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to find a set of representative Pareto optimal solutions that present the best trade‐off between structural and textual similarities of models. The validation results, based on 8 real world models taken from open‐source projects, confirm the effectiveness of our approach, yielding refactoring recommendations with an average correctness of over 80%. In addition, our approach outperforms 5 of the state‐of‐the‐art refactoring approaches.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143783/1/smr1916.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143783/2/smr1916_am.pd
