A model driven and clustering method for service identification directed by metrics. (19th October 2020)

• Record Type: Journal Article
• Authors: Daghaghzadeh, Mohammad; Babamir, Seyed Morteza
• Is Part Of: Software, practice & experience. Volume 51:Number 2(2021)
• Page Start: 449
• Page End: 484
• Publication Date: 2020-10-19
• Subjects: business process model -- cohesion and coupling -- entity model -- genetic algorithm -- service‐oriented architecture -- service identification; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2913
• Languages: English
• ISSNs: 0038-0644

Abstract

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 business entity (BE) model are the useful models that may be used for SI. To this 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 semiautomatized 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 as proper identified services of a service design. Finally, we used three case studies to show the effectiveness of the proposed method and then evaluated the results.