Technological Stages of Neural Network AI Generation of System Program Code Based on Modular Neuro Integration

Artificial intelligence-powered code generation is revolutionizing system software development through the use of machine learning. Neural network generation of software modules of the system is carried out according to prompts in natural language. A trend of vibe coding has emerged in the world of technology – code generation by neural networks from natural language. For example, Cursor via ChatGPT 4.1 can generate various software modules based on descriptions of their functions in natural language. Creating large software systems from generated modules requires integration. Neurocomplexation of software modules is the process of integrating or combining various software modules to create complex systems based on neural models or artificial neural networks. This approach is proposed to be used in the field of artificial intelligence and machine learning to build complex systems where individual modules interact and jointly perform tasks. Promising areas of application are, firstly, the creation of cognitive systems and intelligent ensembles of agents and assistants. Secondly, modeling of thinking and brain function for research in neuroscience, and thirdly, the development of complex solutions in the field of automation and robotics. The key features of the neural network integration process are, firstly, the integration of modules with different functions (recognition, data processing, training). Secondly, the use of neural network algorithms for adaptation and self-training. Thirdly, ensuring the flexibility and scalability of the system.

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Publication Details

Journal
American Journal of Embedded Systems and Applications
Published
2025-10-09
DOI
https://doi.org/10.11648/j.ajesa.20251001.12
Citations
2
Primary Topic
Industrial Technology and Control Systems
Type
article
Field-Weighted Citation Impact
2.43
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article

Technological Stages of Neural Network AI Generation of System Program Code Based on Modular Neuro Integration

Evgeniy Bryndin
2 citations
American Journal of Embedded Systems and Applications
Industrial Technology and Control Systems
2.43
article

Technological Stages of Neural Network AI Generation of System Program Code Based on Modular Neuro Integration

Evgeniy Bryndin
article en
2 citations

Abstract

Artificial intelligence-powered code generation is revolutionizing system software development through the use of machine learning. Neural network generation of software modules of the system is carried out according to prompts in natural language. A trend of vibe coding has emerged in the world of technology – code generation by neural networks from natural language. For example, Cursor via ChatGPT 4.1 can generate various software modules based on descriptions of their functions in natural language. Creating large software systems from generated modules requires integration. Neurocomplexation of software modules is the process of integrating or combining various software modules to create complex systems based on neural models or artificial neural networks. This approach is proposed to be used in the field of artificial intelligence and machine learning to build complex systems where individual modules interact and jointly perform tasks. Promising areas of application are, firstly, the creation of cognitive systems and intelligent ensembles of agents and assistants. Secondly, modeling of thinking and brain function for research in neuroscience, and thirdly, the development of complex solutions in the field of automation and robotics. The key features of the neural network integration process are, firstly, the integration of modules with different functions (recognition, data processing, training). Secondly, the use of neural network algorithms for adaptation and self-training. Thirdly, ensuring the flexibility and scalability of the system.

American Journal of Embedded Systems and ApplicationsVol. 10(1)
Institute of Physiology and Basic Medicine (RU)
Openalex Percentile: Top 9%
Industrial Technology and Control Systems
2.43
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