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

Abstract

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.

Keywords

• machine learning
• natural language processing
• graphical interface
• transfotmers
• tkinter
• deep learning
• automatic code generation