Matching UML class diagrams using a Hybridized Greedy-Genetic algorithm
During the early stages of software development, engineers find themselves dealing with a large collection of models. Lack of efficient management of these models results in duplicated artifacts, ineffective reuse, and an aggravated maintenance effort. Models' matching is at the core of different model management operations such as models' evolution, consolidation, and retrieval. It is a kind of a combinatorial problem. The difficulty of the problem comes in two main streams, the similarity assessment metrics and the matching algorithms. In this paper, we present a greedy-based algorithm for matching UML class diagrams based on their lexical, internal, neighborhood similarity, and a combination of them. Additionally the paper empirically compares the performance of the proposed algorithm against the simulated annealing algorithm in terms of the matching accuracy and time.