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Title: REST API auto generation: A model-based approach
Authors: Hussein, Salah 
Zein, Samer 
Salleh, Norsaremah 
Keywords: Artificial intelligence;Code generation;Computer software - Evaluation - Simulation methods;Software measurement - Simulation methods;Software framework;SOA;Service-oriented architecture (Computer science) - Testing;Web services;REST APIs;Application program interfaces (Computer software);Active server pages
Issue Date: 2020
Publisher: Frontiers in Artificial Intelligence and Applications
Abstract: Most of software products, especially mobile applications (apps) rely on a back-end web services to communicate with a shared data repository. Statistics have demonstrated exponential demand on web services, mainly REST, due to the continuous adoption of IoT (Internet of Things) and Cloud Computing. However, the development of back-end REST web services is not a trivial task, and can be intimidating even for seasoned developers. Despite the fact that there are several studies that focus on automatic generation of REST APIs, we argue that those approaches violate the rules of code flexibility and are not appropriate for novice developers. In this study, we present an approach and a framework, named RAAG (REST Api Auto-Generation), that aims to improve productivity by simplifying the development of REST web services. Our RAAG framework abstracts layers, where code generation has been avoided due its limitations. A preliminary evaluation shows that RAAG can significantly improves development productivity and is easy to operate even by novice developers.
DOI: 10.3233/FAIA200570
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