Code generation system based on MDA and convolutional neural networks

The software industry has rapidly evolved with high performance. This is owing to the implementation of good programming practices and architectures that make it scalable and adaptable. Therefore, a strong incentive is required to develop the processes that initiate this project. We aimed to provide...

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Published in:Frontiers in artificial intelligence Vol. 8; p. 1491958
Main Authors: Vargas-Monroy, Gabriel, Gonzalez-Roldan, Daissi-Bibiana, Montenegro-Marín, Carlos Enrique, Daza-Corredor, Alejandro-Paolo, Leal-Lara, Daniel-David
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 11.03.2025
Subjects:
Artificial Intelligence
artificial vision
clean architecture
computer vision
deep learning
generative programming
MDA
deep learning
MDA
artificial vision
generative programming
clean architecture
computer vision
ISSN:2624-8212, 2624-8212
Online Access:Get full text
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Summary:The software industry has rapidly evolved with high performance. This is owing to the implementation of good programming practices and architectures that make it scalable and adaptable. Therefore, a strong incentive is required to develop the processes that initiate this project. We aimed to provide a platform that streamlines the development process and connects planning, structuring, and development. Specifically, we developed a system that employs computer vision, deep learning, and MDA to generate source code from the diagrams describing the system and the respective study cases, thereby providing solutions to the proposed problems. The results demonstrate the effectiveness of employing computer vision and deep learning techniques to process images and extract relevant information. The infrastructure is designed based on a modular approach employing Celery and Redis, enabling the system to manage asynchronous tasks efficiently. The implementation of image recognition, text analysis, and neural network construction yields promising outcomes in generating source code from diagrams. Despite some challenges related to hardware limitations during the training of the neural network, the system successfully interprets the diagrams and produces artifacts using the MDA approach. Plugins and DSLs enhance flexibility by supporting various programming languages and automating code deployment on platforms such as GitHub and Heroku.
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Gururaj T., Visvesvaraya Technological University, India
Edited by: Pradeep Nijalingappa, Bapuji Institute of Engineering and Technology, India
Reviewed by: Antonio Sarasa-Cabezuelo, Complutense University of Madrid, Spain
ISSN:2624-8212
2624-8212
DOI:10.3389/frai.2025.1491958