Automated Code Generation using Machine Learning Techniques

Abstract

Automated code generation using machine learning techniques has
emerged as a transformative approach to software development, enabling
developers to generate high-quality code with minimal manual effort. This
paper explores recent advancements in machine learning-driven code
generation, focusing on deep learning models, transformer-based
architectures, and large language models (LLMs) such as GPT, CodeBERT,
and Codex. Key methodologies include natural language processing (NLP)
for translating human-readable descriptions into executable code,
reinforcement learning for improving code efficiency, and fine-tuning
techniques to enhance model adaptability. We discuss various
applications, including code completion, bug fixing, and optimization,
while addressing challenges such as code correctness, security
vulnerabilities, and domain-specific adaptations. Finally, we highlight
future directions, emphasizing the need for explainability, improved
dataset quality, and hybrid AI-human collaboration for robust and efficient
automated code generation.