Tool Support for Clustering Large Meta-models

Ever-growing requirements, long-term evolution and mod-ernization of software projects lead to meta-models of re-markable size, being difficult to comprehend and maintain. This paper presents a tool that supports the decomposition of a meta-model into clusters of model elements. Methods proposed in the research area of graph clustering, aiming at the desired properties of high cohesion and low coupling, have been integrated in the tool. The methods are cus-tomized not only to utilize the underlying graph structure, but also the semantic information given in meta-models. An evaluation of the tool is provided in terms of a case study