Similarity assessment of UML class diagrams using a greedy 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.