Enhanced model-driven web application development with code generation using deep learning technique

Abstract

Advancements in modern technology have led to an endless reliance on the Internet. This has created a great demand for the fast and accurate development of web applications. Web development has benefitted from programming approaches such as Model-Driven Web Engineering (MDWE). MDWE allows developers to choose pre-defined models and utilize them for their requirements. This kind of structural blueprinting, called wireframing, enables efficiency in software development. However, these techniques are seldom understood by people without a technological background. Hence, much of the coding central to a project remains the responsibility of a few tech-educated people. This work proposes an approach that offloads some of the typing to a machine-based code generator. This has been achieved by pairing MDWE methods with Deep Learning capabilities. This ensures a less coding-intensive web development methodology that can be utilized even by non-web developers. This work makes several contributions to improving overall MDWE methods.