A Pilot Study on AI-Assisted Code Generation with Large Language Models for Software Engineering

The field of code generation, influenced by deep learning, has become crucial in contemporary software engineering, facilitating the conversion of natural language to executable code. A noticeable knowledge gap exists, prompting an exhaustive examination of the current methodologies and innovations. The primary objective of this research is to offer a thorough literature review, illuminating the current state of deep learning-powered code generation. A rigorous systematic review was employed, wherein 28 influential papers from essential academic databases were recognized. An analytical approach was adopted to discern trends, understand the significance of numbers, and draw meaningful conclusions from the data. These papers were then analyzed using a structured methodology. The study unveils insights into large language model code generation, potentially bridging the prevailing knowledge gap and offering direction for future innovations in the domain.