Service Classification through Machine Learning: Aiding in the Efficient Identification of Reusable Assets in Cloud Application Development

Abstract

Developing software based on services is one of the most emerging programming paradigms in software development. Service-based software development relies on the composition of services (i.e., pieces of code already built and deployed in the cloud) through orchestrated API calls. Black-box reuse can play a prominent role when using this programming paradigm, in the sense that identifying and reusing already existing/deployed services can save substantial development effort. According to the literature, identifying reusable assets (i.e., components, classes, or services) is more successful and efficient when the discovery process is domain-specific. To facilitate domain-specific service discovery, we propose a service classification approach that can categorize services to an application domain, given only the service description. To validate the accuracy of our classification approach, we have trained a machine-learning model on thousands of open-source services and tested it on 67 services developed within two companies employing servicebased software development. The study results suggest that the classification algorithm can perform adequately in a test set that does not overlap with the training set; thus, being (with some confidence) transferable to other industrial cases. Additionally, we expand the body of knowledge on software categorization by highlighting sets of domains that consist 'grey-zones' in service classification.

Eclipse Open SmartCLIDE: An end-to-end framework for facilitating service reuse in cloud development

“…[PHASE-2: REUSABLE ASSET IDENTIFICATION] The services in this model correspond to the candidates for reuse, which are then attempted to be identified (Alizadehsani. et al, 2022) (service discovery) in the SmartCLIDE repository (in house, domain-specific, domain-agnostic services are queried).…”