Generative AI-Driven Approach to Converting Numerical Code into Mathematical Functions

In the era of Artificial Intelligence (AI), the role of Generative AI models has witnessed a paradigm shift in addressing programming challenges. This research delves into the evolving landscape of Generative AI-driven code generation for programming problems, with a focus on the convergence behavior, algorithmic efficiency, learning dynamics, and code-length-performance dynamics of AI models, exemplified by ChatGPT and BARD. Through meticulous experimentation and analysis, this study elucidates key findings that illuminate the strengths and nuances of these models in code generation contexts. ChatGPT demonstrates rapid initial convergence, making it advantageous in time-sensitive scenarios, while BARD exhibits potential for precision-critical tasks. Algorithmic efficiency comparisons reveal subtle differences, emphasizing the need for further investigation into runtime influences. Learning curve analysis underscores the significance of early iterations in training, while code length-performance dynamics present intriguing correlations that warrant in-depth exploration. These insights provide a foundation for informed model selection and training strategies, while highlighting the ethical considerations and future avenues in AI-driven code generation.