Large Language Models on Software Refactoring

The recent surge in popularity of large language models (LLM) such as OpenAI's GPT series of models has raised interest in the application of artificial intelligence (AI) in different fields, including software engineering. AI-powered programming assistants and code generation tools, namely GitHub Copilot, have decisively paved their way into common integrated development environments to provide feedback for software developers with programming tasks. These tasks may range from producing new software code, to providing suggestions for a programming task, to parsing through written code and analyzing it for potential improvements. Maintaining existing software is a very resource intensive task. Software programs are rarely written in a way that do not need adjustments later on. Addressing bugs and introducing new features are common tasks for developers, which require a reasonable understanding of the code they are working on. Refactoring is the process of rewriting software code in an attempt to streamline further development and maintenance or to improve the underlying design of the software without making changes to the overall functionality of the software. The thesis begins with an introduction to the topic of software quality. This aims to give a thorough understanding on which factors contribute to the quality of software. An overview of LLMs is presented as well as how they are generally applied to code generation. This thesis explores the extent to which a selection of four different LLMs can be utilized in code refactoring and what kinds of refactorings these LLMs are able to determine. This research is done by giving each LLM a piece of code to refactor. The outputs of these models are then evaluated on a variety of software metrics and commonly accepted code refactoring practices. With these results we can assess whether these language models could be applied in practice.