Abstract
Model transformation methods tend to create many transformation rules, but there is still some redundancy among these transformation rules. Therefore, how to obtain effective transformation rules has become an unsolved important problem. However, current model transformation methods do not focus on these rules. Therefore, we propose a clustering-based method for the selection of transformation rules. The main idea is to classify the target model elements, transform the rules of each class and finally, obtain the appropriate conversion rules via the post-clustering conversion rules. We also present an algorithm to automatically validate the optimal selection of model transformation rules. A motivating example is presented to illustrate our approach. Furthermore, the comparison experiments of these algorithms are conducted, which have proved the effectiveness of the optimal selection method.
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