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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Code generation system based on MDA and convolutional neural networks.

Gabriel Vargas-Monroy1, Daissi-Bibiana Gonzalez-Roldan1, Carlos Enrique Montenegro-Marín1

  • 1Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia.

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This summary is machine-generated.

This study introduces a novel platform using computer vision and deep learning to automatically generate source code from system diagrams. This streamlines software development, enhancing scalability and adaptability.

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MDAartificial visionclean architecturecomputer visiondeep learninggenerative programming

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • The software industry demands scalable and adaptable architectures.
  • Efficient development processes are crucial for high-performance software.

Purpose of the Study:

  • To develop a platform streamlining software development by connecting planning, structuring, and coding.
  • To generate source code from system diagrams using advanced AI techniques.

Main Methods:

  • Employed computer vision and deep learning for image processing and information extraction.
  • Utilized Model-Driven Architecture (MDA) to generate source code from diagrams.
  • Implemented a modular infrastructure with Celery and Redis for asynchronous task management.

Main Results:

  • Demonstrated the effectiveness of computer vision and deep learning in interpreting diagrams for code generation.
  • Successfully produced software artifacts from diagrams using the MDA approach.
  • Enhanced flexibility with plugins and Domain-Specific Languages (DSLs) for multi-language support and automated deployment.

Conclusions:

  • The developed system effectively generates source code from diagrams, addressing challenges in software development.
  • The integration of computer vision, deep learning, and MDA offers a promising approach to automated software engineering.
  • The modular design and extensibility ensure efficient and adaptable code generation for various platforms.