A model driven and clustering method for service identification directed by metrics

Abstract

Service identification (SI) in the life cycle of service-oriented architecture is a critical phase. Business models consisting of business process (BP) model and businessentity(BE)modelaretheusefulmodelsthatmaybeusedforSI.Tothis end, SI is carried out by partitioning activities in BP based on the activities’ use of the entities in BE. However, a proper partitioning activities to services, which is called a service design, is a challenge. This article aims to present a semi- automatized clustering method for partitioning the activities to services, which is directed by new proposed metrics cohesion, coupling, and granularity. With regard to the conflict of the metrics, a multiobjective evolutionary algorithm (MOEA) is used to clustering activities where the metrics are considered as objectives should be optimized. The MOEA produces a set of optimal solutions asproperidentifiedservicesofaservicedesign.Finally,weusedthreecasestud- ies to show the effectiveness of the proposed method and then evaluated the results.