Applying genetic algorithm for automatic service identification based on quality metrics

Service-oriented architecture improves the stability and operational capability of software systems for passive defense measures. Automatic identification of services using quality of service measures ensures the successful deployment of service-oriented architecture and is great importance to speed up software development life cycle. Little attention to non-functional requirements، no considerations for concurrent effects of business activities and entities and non-automated ranking of candidate services are the major issues with current approaches. The approach proposed in this paper considers both the business processes and entities، simultaneously to detect services. Applying a genetic algorithm، candidate services are identified based on quality metrics i. e. granularity، coupling، cohesion and convergence. These metrics are obtained from breaking goals to requirements of level. The TOPSIS method is applied to rank the candidate services. The illustrated case study is shown that high quality services can be identified automatically with minimal software developer’s interventions.