ENHANCING AUTOMATED CODE GENERATION WITH TRANSFORMER MODELS AND REINFORCEMENT LEARNING: A DEEP LEARNING APPROACH TO SOFTWARE DEVELOPMENT

Automated programming using deep learning can shorten the code development process and ensure the software built is of high quality. In this paper, we investigate the combination of transformer models and reinforcement learning (RL) for the automatic generation of code. The aim is to design a model that produces correct, consistent, and valuable code in different programming languages. Our test utilized 5 million pieces of code from several open-source repositories, and models were evaluated based on whether their output was grammatically correct, code quality, code execution accuracy, and code production speed. Our model outperforms conventional LSTM-based approaches and GPT-2, achieving excellent syntactic correctness and execution accuracy, the highest code quality marks (8.5/10), and completing tasks in less time. The findings demonstrate that combining deep learning and RL enables the creation of top-quality code efficiently. By applying AI to software development, this work finds that both speed and reliability noticeably improve, which is beneficial for all parties involved and the broader industry. Despite the success of deep learning in natural language processing, automated code generation continues to face challenges related to execution correctness, code quality, and scalability across programming languages. Experimental results demonstrated that the proposed transformer–reinforcement learning framework achieved higher syntactic correctness, execution accuracy, and reduced generation time compared to existing LSTM-based and transformer-only models, indicating its suitability for real-world software development tasks.