GENERATIVE AI FOR AUTOMATIC CODE GENERATION

The Generative AI for Automatic Code Generation System demonstrates the increasing power of Artificial Intelligence and Natural Language Processing in revolutionizing the way software is created. By harnessing the strength of Deep Learning the Transformer model as well as the best natural language processing the system has the capability to translate natural language statements into code. This will make the process of coding obsolete such that a beginner with basic coding skills will be able to develop programs without much effort. Conventional coding by the methods to techniques always demands significant technical skill. On the contrary this method which employs the use of AI is able to produce codes in a highly precise effective and quicker manner. The code-producing system is able to quickly decipher the directed meaning of the user logics and produce high-quality optimized and executable codes in different programming languages such as Python Java C++ among others. Of course this indicates that this system is capable of being trustworthy flexible and useful in an academic as well as an industrial context. The project successfully meets its intended target since it embeds a powerful AI engine in addition to having a user-friendly interface. The web interface designed on Flask allows for smooth interaction and the AI logic at the backend provides accurate results.

1.   Introduced the Transformer architecture in their NeurIPS publication “Attention Is All You Need” which forms the foundation for modern Generative AI models used in automatic code generation. 2.  Brown T. B. et al. (2020) presented the GPT family of models in the paper “Language Models are the Few-Shot Learners” demonstrating how large-scale language models can understand natural language and generate code effectively. 3.    Wolf T. et al. (2020) described the Hugging Face Transformers library in their EMNLP paper highlighting state-of-the-art NLP techniques that support advanced generative and analytical tasks in AI-based systems. 4.  Chollet F. (2015) documented the Keras deep learning library providing essential tools and frameworks for building training and deploying neural networks used in AI-driven applications. 5.     Paszke A. et al. (2019) introduced PyTorch as a high-performance flexible deep learning framework in their NeurIPS publication making it suitable for implementing transformer and NLP models in this project. 6.    OpenAI (n.d.) provides official API documentation for GPT models on their developer platform detailing how generative