Treffer: Artificial neural network model for automatic code generation in graphical interface applications: .

Title:
Artificial neural network model for automatic code generation in graphical interface applications: .
Authors:
Arenas Varela, Daniel Esteban, Muñoz Ordóñez, Julián Fernando
Source:
INGE CUC, ISSN 2382-4700, Vol. 19, Nº. 1, 2023 (Ejemplar dedicado a: (Enero - Junio))
Publisher Information:
2023
Document Type:
E-Ressource Electronic Resource
Index Terms:
Machine learning, Natural language processing, Graphical interface, Transfotmers, Tkinter, Deep learning, Automatic code generation, Aprendizaje automático, procesamiento de lenguaje natural, interfaz gráfica, transformadores, aprendizaje profundogeneración automática de código, text (article)
URL:
https://dialnet.unirioja.es/servlet/oaiart?codigo=8852005
Availability:
Open access content. Open access content
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Note:
application/pdf
INGE CUC, ISSN 2382-4700, Vol. 19, Nº. 1, 2023 (Ejemplar dedicado a: (Enero - Junio))
English
Other Numbers:
S9M oai:dialnet.unirioja.es:ART0001586795
https://dialnet.unirioja.es/servlet/oaiart?codigo=8852005
(Revista) ISSN 0122-6517
(Revista) ISSN 2382-4700
1376708971
Contributing Source:
UNIV COMPLUTENSE DE MADRID
From OAIster®, provided by the OCLC Cooperative.
Accession Number:
edsoai.on1376708971
Database:
OAIster

Weitere Informationen

Introduction: Currently, the software development industry is living in its golden age due to the progress in areas related to machine learning, which is part of AI techniques. These advances have allowed tasks considered exclusively human to be solved using a computer. However, the complexity and the extensive area covered by new projects that must be developed using programming languages have slowed down project delivery times and affected the company's productivity. Objective: This research presents the methodology carried out for constructing a recurrent neural network model for the automatic generation of source code related to graphical user interfaces using Python programming language. Method: By constructing a natural language-related dataset for describing graphical interfaces programmed in Python, a deep neural network model is built to generate automatic source code. Results: The trained model achieves loss and perplexity values of 1.57 and 4.82, respectively, in the validation stage, avoiding overfitting in the model's training. Conclusions: A neural network model is trained to process the natural language related to the request to create graphical interfaces using the Python programming language to automatically generate source code that can be executed through the Python interpreter.