What is AI code refactoring?

What is AI code refactoring?

AI code refactoring is the process of using artificial intelligence to improve the internal structure of code without changing its external behavior. These improvements might involve simplifying logic, reducing duplication, restructuring functions or classes, or cleaning up technical debt — all with minimal developer intervention.

AI for code refactoring enables teams to maintain high code quality even as complexity grows, allowing developers to focus on features instead of formatting and structural cleanup.

Why use AI for code refactoring?

Manual refactoring is tedious, error-prone, and often neglected due to tight deadlines. With AI-assisted code refactoring, developers gain a productivity boost while ensuring maintainability across the codebase. Key benefits include:

• Time savings: Refactor large chunks of code in seconds, not hours.
• Consistency: Enforce team conventions and standards automatically.
• Reduced tech debt: Clean up legacy code incrementally during development.
• Improved code reviews: Cleaner code is easier to understand and approve.

Whether you're maintaining a legacy system or scaling a fast-moving team, AI for refactoring code helps keep things under control.

How AI performs code refactoring

AI-assisted tools typically parse your code into an abstract syntax tree (AST), analyze the structure, and match it against learned patterns or predefined best practices.

Examples of common AI-powered refactoring:

Before:

def get_total(items):
    total = 0
    for item in items:
        total += item
    return total

After (AI-refactored):

def get_total(items):
    return sum(items)

Other examples include:

• Extracting repeated logic into reusable functions
• Converting imperative loops into functional constructs
• Identifying and removing dead or unreachable code
• Replacing deprecated APIs

AI tools for code refactoring

A number of tools are entering the space of AI-assisted code refactoring. Here are a few worth knowing:

Graphite

While Graphite doesn't perform code refactoring automatically, it facilitates the management of code changes. Graphite integrates directly with GitHub and enhances developer productivity through AI-driven review. It supports:

• Automated PR summaries
• Inline suggested changes from code comments
• Automatic PR splitting (for large or tangled changes)
• Stacked PRs to build on top of open changes without blocking

It’s a powerful tool for teams aiming to refactor in small, reviewable increments. Graphite also works well with existing Git workflows and editor integrations like VS Code.

CodiumAI

CodiumAI focuses on generating tests and validating code behavior. It also includes features for refactoring, particularly around logic simplification and code smell detection. It’s ideal for individual developers or small teams wanting lightweight AI suggestions directly in the IDE.

Sourcegraph Cody

Cody is Sourcegraph’s AI coding assistant designed to work across entire codebases. It understands your code in context and can:

• Refactor code using code graph awareness
• Perform semantic search across large codebases
• Suggest improvements with understanding of architecture and dependencies

Cody is ideal for large teams or monorepos where contextual awareness is key.

Refact.ai

Refact.ai takes a more language-model-centered approach, using AI to assist with code completions, refactoring, and commenting. It’s highly configurable and supports multi-language environments.

When to refactor code with AI

AI for refactoring code is most effective when:

• Cleaning up legacy code before modernization
• Refactoring after feature delivery
• Keeping a growing codebase consistent across teams
• Migrating between frameworks or language versions

It’s not a silver bullet — architectural decisions still require human judgment — but for repetitive or mechanical changes, AI is a major time-saver.

Conclusion

AI-assisted code refactoring brings automation and intelligence to one of the most neglected areas of development: code hygiene. By using tools like the ones we covered, teams can offload the repetitive parts of code improvement and focus on the bigger picture.

Whether you're maintaining a legacy monolith or iterating fast in a startup, using code optimization with AI is a smart move for staying agile and shipping confidently.