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Service-Oriented Architecture (SOA) has gained considerable popularity for the development of distributed enterprise-wide applications within the software industry. The SOA paradigm promotes the reusability and integrability of software in heterogeneous environments by means of open standards. Most software companies capitalize on SOA by discovering and composing services already accessible over the Internet, whereas other organizations need internal control of applications and develop new services with quality-attribute properties tailored to their particular environment. Therefore, based on architectural and business requirements, developers can elaborate different alternatives within a SOA framework to design their software applications. Each of these alternatives will imply trade-offs among quality attributes, such as performance, dependability and availability, among others. In this context, Artificial Intelligence (AI) can assist developers in dealing with service-oriented design with the positive impact on scalability and management of generic quality attributes. In this paper, we offer a detailed, conceptualized and synthesized analysis of AI research works that have aimed at discovering, composing, or developing services. We also identify open research issues and challenges in the aforementioned research areas. The results of the characterization of 69 contemporary approaches and potential research directions for the areas are also shown. It is concluded that AI has aimed at exploiting the semantic resources and achieving quality-attribute properties so as to produce flexible and adaptive-to-change service discovery, composition, and development.
Rodríguez et al. (Fri,) studied this question.
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