Generative Ai Coding Tech - An Advance Pathway

Abstract

The evolution of generative AI, driven by advancements in large
language models (LLMs) and deep learning, has revolutionized
the field of code generation, offering transformative solutions for
software development. This paper explores state-of- the-art
frameworks like StepCoder and AI Programmer, which leverage
reinforcement learning (RL) and genetic algorithms (GAs),
respectively, to address challenges in generating complex code
sequences and optimizing program functionality. StepCoder
introduces a curriculum-based approach to break down lengthy
code tasks and fine-grained optimization techniques to enhance
code quality, while AI Programmer employs constrained
programming languages and genome-based techniques to
autonomously generate full software programs. Additionally,
this study examines the integration of generative AI into
competitive programming tasks and its ability to predict
program functions by fusing neural network predictions with
search algorithms. Generative AI tools streamline coding
processes by automating repetitive tasks, modernizing legacy
systems, and translating code across languages. These
innovations not only enhance productivity but also democratize
coding by making it accessible to developers of all skill levels.
The paper further discusses the implications of generative AI in
computer science education and professional programming,
emphasizing the need for actionable strategies to harness its
benefits while addressing ethical concerns and technical
limitations. By analyzing experimental results and real-world
applications, this research highlights how generative AI is
reshaping software engineering, fostering collaboration between
human creativity and AI-driven automation, and paving the way
for more efficient and innovative coding practices