  In this paper, we present a novel approach to improving software quality and
efficiency through a Large Language Model (LLM)-based model designed to review
code and identify potential issues. Our proposed LLM-based AI agent model is
trained on large code repositories. This training includes code reviews, bug
reports, and documentation of best practices. It aims to detect code smells,
identify potential bugs, provide suggestions for improvement, and optimize the
code. Unlike traditional static code analysis tools, our LLM-based AI agent has
the ability to predict future potential risks in the code. This supports a dual
goal of improving code quality and enhancing developer education by encouraging
a deeper understanding of best practices and efficient coding techniques.
Furthermore, we explore the model's effectiveness in suggesting improvements
that significantly reduce post-release bugs and enhance code review processes,
as evidenced by an analysis of developer sentiment toward LLM feedback. For
future work, we aim to assess the accuracy and efficiency of LLM-generated
documentation updates in comparison to manual methods. This will involve an
empirical study focusing on manually conducted code reviews to identify code
smells and bugs, alongside an evaluation of best practice documentation,
augmented by insights from developer discussions and code reviews. Our goal is
to not only refine the accuracy of our LLM-based tool but also to underscore
its potential in streamlining the software development lifecycle through
proactive code improvement and education.
