An analytical automated refinement approach for structural modeling large-scale codes using reverse engineering

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In this paper, an analytical automated refinement approach is presented to facilitate the behavioral modeling large-scale codes using reverse engineering methods. First, the relation features of the code structure are extracted using Understand tool. The structural model of the large-scale code is presented in forms of class diagrams. A middleware application is presented to translate the extracted features to class relations. Two evolutionary algorithms are addressed for clustering the existing classes. Finally, by using a converter, we transform the cluster class relations to an executive file in Rational Rose. The response time of the clustering with our approach is lower than the other algorithms.