AI-Powered Coding Tools: A Study of Advancements, Challenges, and Future Directions

Abstract

This review examines the effects of AI-assisted programming in contemporary software development, paying particular attention to tools driven by Large Language Models (LLMs), such as GPT-4o and GitHub Copilot. Starting with data processing and ending with model deployment, the review describes the standard workflow for training machine learning models and how programmers use these models to improve their coding processes. GitHub Copilot, an AI-powered code generator, and GPT-4o, a general-purpose LLM, are compared in terms of accuracy, usability, and efficiency when assisting with programming tasks. The results show that although both tools greatly facilitate coding, they each have particular advantages and disadvantages. GitHub Copilot is excellent at integrating with IDEs, providing contextual code recommendations and streamlining processes. In contrast, GPT-4o shows better accuracy when creating code from scratch, but it does not have Copilot’s seamless IDE integration. The review also identifies some of the current drawbacks of AI-powered coding tools, including the possibility of producing faulty or vulnerable code as a result of training on unreliable datasets and the inability to comprehend context, which can occasionally result in functionally correct but practically incorrect code. In order to filter and fix problematic code before training, the review recommends using advanced algorithms for data pre-processing. It also suggests improving the interpretability of code generated by AI to help developers better comprehend and trust the results.