What is AI code refactoring?

AI code refactoring employs artificial intelligence to automate the process of refactoring code. It uses machine learning and natural language processing (NLP) techniques to modify software code’s internal structure while keeping the external behavior or functionality intact.

Code refactoring is an integral part of the software development process, reducing technical debt through code improvements that optimize performance and lead to better readability and enhanced maintainability. With the help of AI, refactoring can be more efficient.

AI code refactoring relies on large language models (LLMs). These LLMs have been trained on vast codebases, learning code structure in different programming languages. Code LLMs can then be fine-tuned for use cases like code generation, debugging, modernizing legacy code and code refactoring.

Many AI tools implement one or a combination of these concepts to refactor code:

• Lexical pattern matching

• Abstract syntax tree

• Lossless semantic tree

• Reinforcement learning

AI models might treat software code as a sequence of tokens, applying lexical pattern matching to spot common inefficiencies and “sniff” typical “code smells,” including complex conditionals, duplicate functions, monolithic modules, overly long methods or unclear variable names. Based on these recognized patterns, models recommend a refactoring transformation in real time, such asextract function to break down a huge function into smaller snippets, move method to relocate a method from a subclass to a superclass for reusability and less code duplication, andrename variable for more descriptive variable names.

Some AI-powered code refactoring systems might opt to represent source code as an abstract syntax tree or AST, which captures code hierarchy in terms of structure and syntax. These systems parse existing code into an AST, preprocess the AST into sequential or structural data and encode the data into vector embeddings that neural networks can process.¹

Models then manipulate ASTs, applying refactoring transformations that result in a new code state. This new code state must still compile and run correctly, preserve expected behaviors and maintain the logical structure of the entire codebase.

Other AI coding assistants use lossless semantic trees (LSTs). LSTs preserve style formatting and include type information for each code element.² Such type attribution allows for safer semantic code searches that move beyond syntax to meaning, plus safer dependency upgrades and framework migrations.³

Reinforcement learning algorithms train machine learning models to take actions that garner the greatest reward. In the case of code refactoring, models learn which refactoring transformations lead to better outcomes through metrics such as faster execution time, more passed unit tests and integration tests or increased test coverage. Models also consider developer feedback, learning when software developers accept or reject code refactoring suggestions.

Software engineering teams that use AI for code refactoring can gain these advantages:

• Better code quality

• Improved developer productivity

• Scalability

• Swifter speed

Manual refactoring can be error-prone, carrying the possibility of creating a new bug or resurfacing an existing one. Employing AI helps decrease this possibility, introducing fewer bugs during the code refactoring process and establishing consistency when it comes to coding standards. Some AI-driven refactoring systems even offer the ability to integrate with CI/CD pipelines, helping maintain code quality throughout the software development lifecycle.

ng on behalf of developers, allowing them to tackle more complex code refactoring. Many AI tools also seamlessly link with most integrated development environments (IDEs) so as not to disrupt software development workflows.

Code refactoring can be challenging, especially for large codebases. AI-driven refactoring helps streamline the process through AI agents tasked with implementing changes on a massive scale.

Manual refactoring can take hours, particularly for projects with elaborate dependencies. AI systems can accelerate refactoring, analyzing code and generating suggestions in seconds, saving valuable human developer hours.

AI can be a powerful technology, but it’s not without its challenges. Here’s what enterprises need to consider when using AI for code refactoring:

• Start small

• Verify and test

• The human touch remains vital

Software engineering teams can begin using AI for minor changes like deleting dead code or fixing formatting. This limited scope allows developers to experiment with AI refactoring tools while still maintaining control over their code.

One study found that agentic AI excels in low-level, consistency-oriented refactoring tasks, including renaming and type changes. The study also noted that agentic coding tools act as “incremental cleanup partners” since they produce small yet statistically significant structural enhancements, such as reducing class size and complexity.⁴

AI tools can’t replace a human developer’s expertise and judgement, which means software engineers must always review AI suggestions or AI-generated refactoring transformations to make sure they’re accurate. Additionally, developers must run comprehensive tests to validate that any affected feature or functionality works as expected.

In the study mentioned earlier, researchers found that maintainability and readability are the main drivers of agentic refactoring, while human developers more frequently address design-level concerns.⁴ AI systems often lack contextual awareness, and as such struggle with tasks involving intricate logic or sophisticated architecture. This high-level refactoring remains within the purview of human programmers, who can then delegate routine cleanup to AI agents.

Organizations must first find the right fit before adopting a tool. Factors to take into account include capabilities that align with project goals and requirements, compatibility with a team’s development workflow and tech stack, and pricing models that suit business budgets, among others.

Here are some AI-powered code refactoring technologies to choose from:

• LLMs

• AI coding assistants

• Agentic coding tools

Some popular examples of code LLMs that can be used for refactoring include Google’s CodeGemma, Meta’s Code Llama and Mistral’s Codestral. Open-source code models include DeepSeek Coder and Qwen3-Coder. Other LLMs that aren’t solely for programming but have been trained and optimized for coding include the latest versions of Anthropic’s Claude Sonnet and Opus, Google’s Gemini and OpenAI’s GPT.

Development teams might consider fine-tuning these LLMs on their own codebases, allowing models to generate tailored refactoring recommendations. However, teams must also check with model providers to make sure any sensitive data or proprietary code remains private and secure.

AI coding assistants blend generative AI and automation to help developers refactor their codebases. They support major programming languages like Java, JavaScript, Python and TypeScript and integrate with popular IDEs such as Eclipse, the JetBrains suite, Visual Studio and VS Code. Examples of AI coding assistants include Amazon Q Developer, CodeScene ACE, Gemini Code Assist, GitHub Copilot, Mistral Code and Tabnine.

Agentic coding tools are a step up from AI coding assistants, autonomously refactoring code. For instance, IBM Bob has a code review mode that goes beyond linting to statically analyze code for potential errors, understanding developer intent, flagging issues early and explaining its reasoning. Other AI agents for refactoring include Claude Code, Moderne’s Moddy, Qwen Code and Refact.ai.