By the end of this guide you will have refactored a piece of legacy code in small, reviewed steps, with tests that prove the behavior did not change and a clean history you can roll back. The job is done by an AI coding agent or assistant that reads the code, proposes changes and applies them to your repository, while you set the goal, protect behavior with tests and review every diff.
If you want to refactor legacy code with AI, the central choice is how much autonomy to give the tool. This guide shows a cautious, test-first method and compares seven tools from our Coding category, from terminal agents to code-graph platforms.
Key takeaways
- Pin down current behavior with tests before changing anything, then refactor in small commits that you review one at a time.
- The main decision is autonomy and context: a terminal or editor agent that edits files, a bulk test generator for Java and Python, or a platform that maps dependencies before you touch the code.
- Free options exist but are limited: Aider is open source and charges nothing itself, Potpie's Individual Pro tier is $0 with 50 requests a month, and Refact.ai has a Free plan.
- AI refactors can change behavior silently. Never merge a large automated change without passing tests and a human review.
What you need before you start
- A repository under version control. Every step should be a commit you can revert.
- A way to run the code and tests. If the build is broken today, fix that first.
- A clear goal. For example: split a very long function, remove duplication, upgrade a framework or move to a newer language version.
- A small target. Choose one module for the first pass, not the whole system.
- Time. A first small refactor takes an afternoon including review; larger migrations take much longer.
- Difficulty. Moderate to hard, because the risk is in behavior you may not fully understand.
- Code policy. Check what your organization allows before sending proprietary code to a model provider.
Which kind of AI tool fits this job
Match the tool to how risky and how large the change is.
Agents that edit files in your repo. Claude Code takes jobs such as fixing bugs, writing tests or carrying out a migration, and works through the project while you steer and review. It lists checkpoints and sandboxing for safer edits. Aider runs in your terminal, edits files in your local git repository and commits each change with a generated message, so ordinary git commands can diff or undo what the AI did. Cursor is an AI code editor built around an agent that plans and carries out multi-step programming tasks.
An agent inside your IDE with rollback. Refact.ai is an open-source agent for VS Code and JetBrains that plans and runs tasks, can roll back changes it made, and supports your own API keys and self-hosting.
Tools that build a safety net of tests. Diffblue Cover writes unit tests for Java and Python, checks that each generated test compiles, passes and adds coverage, and can work through a whole repository in batch mode.
Tools that map the code first. Potpie builds a context graph from code, dependencies and history and runs agents for impact analysis and root-cause analysis. ContextStream indexes your repositories and offers dependency and call graphs that show what a change touches, plus shared memory of decisions across coding tools.
Step 1: Define the refactor and what must not change
A refactor changes structure, not behavior, so say what behavior is protected.
- Write the goal in one sentence, such as "split the order-processing function into smaller functions".
- List the behaviors that must stay the same: inputs, outputs, side effects and error cases.
- Note what is out of scope, such as public interfaces other teams depend on.
- Decide how you will know it is done.
Check it worked by being able to point at a test or manual check for each protected behavior.
Step 2: Map what the code touches
Understand the blast radius before the tool changes anything.
- Find callers, dependencies and shared data for the target code.
- Use a code-graph tool if the codebase is large. Potpie offers impact analysis agents, and ContextStream offers dependency and call graphs with change impact analysis.
- Ask your agent to summarize what the module does and what depends on it, then verify the summary by reading the code.
- Write down the risky spots: global state, hidden side effects and code with no tests.
Check it worked by having a list of everything the change could affect that a teammate has reviewed.
Step 3: Build a safety net of tests
Tests are what let you move fast without breaking behavior.
- Run the existing tests and record which pass.
- For Java or Python, a bulk generator such as Diffblue Cover can add tests that pass on the current code. Otherwise ask an agent to write tests that describe present behavior, including odd behavior.
- Review the tests before relying on them. They should record what the code does today, even where that looks wrong.
- Commit the tests on their own, before any refactor.
Check it worked by seeing the new tests pass on the unchanged code.
Step 4: Set up the tool and work on a branch
Contain the work so it is easy to review and undo.
- Create a branch for the refactor.
- Open your agent in the project: a terminal tool such as Aider or Claude Code, or an editor such as Cursor or Refact.ai.
- Give it the goal, the protected behaviors and the list of out-of-scope code. Tell it to make small changes.
- If the tool supports it, turn on safeguards such as checkpoints or rollbacks. Claude Code lists checkpoints, and Refact.ai lists rollback of AI-made changes.
Check it worked by running a harmless request and seeing the tool read the right files.
Step 5: Refactor in small, reviewed steps
Small steps keep bugs findable.
- Ask for one change at a time, such as extracting one function or renaming one concept.
- Run the tests after each change. Aider can lint and run tests after each edit and try to repair failures, but you should still review the result.
- Read the diff before you commit. Look for removed checks, changed conditions and deleted comments.
- Commit each step with a clear message, so you can bisect or revert one step.
Check it worked by having a series of small commits with tests passing at every one.
Step 6: Verify behavior beyond the tests
Passing tests are necessary but not sufficient for old code.
- Run the application and exercise the paths the tests do not cover.
- Compare outputs before and after for a sample of real inputs, if you can do so safely.
- Review performance and logging, since refactors can change both.
- Ask a second developer to review the whole branch, especially anything touching money, security or personal data.
Check it worked by finding no difference between old and new behavior on your sample.
Step 7: Merge, monitor and record what changed
Finish the work so the next person understands it.
- Open a pull request with the goal, the protected behaviors and the test evidence.
- Merge when review and tests pass, and watch logs and error rates after release.
- Record decisions for later sessions. ContextStream stores decisions and lessons across tools, but a short note in the repository works with any tool.
- Plan the next module and repeat, keeping each pass small.
Check it worked by seeing stable behavior in production after release.
Tools for this job compared
| Tool | Good fit for | Pricing | Stated limits |
|---|---|---|---|
| Claude Code | Agent in terminal, IDE and web for migrations and fixes | Paid | Cloud sessions, the desktop app and Slack need a claude.ai subscription; API usage billed per token |
| Cursor | AI code editor with an agent and CLI | Freemium, paid from $8/mo (checked 24 Oct 2026) | Free plan has limited agent and chat requests; third-party model usage billed separately |
| Aider | Terminal edits to a local git repo, with automatic commits | Open source | You supply your own API key or local model; quality depends on the model you connect |
| Diffblue Cover | Safety-net unit tests for Java and Python | Freemium, paid from $1,500, usage-based (checked 24 Oct 2026) | Java and Python only; Community edition capped at 25 methods per month; works per project or module |
| Potpie | Impact analysis on a context graph, self-hosted or in your VPC | Freemium | Individual Pro includes 50 requests per month unless you bring your own keys; self-hosting takes engineering setup |
| Refact.ai | Open-source IDE agent with rollbacks and BYOK | Freemium, paid from $10/mo (checked 24 Oct 2026) | Agent work uses coins and pauses at zero balance; pricing details come from a 2025 announcement |
| ContextStream | Code search, call graphs and shared project memory | Freemium, paid from $19/mo (checked 24 Oct 2026) | Metered in Context Credits; Starter limited to 1 user and up to 15 projects; assistant model charges billed separately |
Prices and plan limits change, so confirm the current terms on each vendor's site before you subscribe.
Common mistakes to avoid
- Refactoring without tests. Without a safety net you cannot tell a clean refactor from a quiet regression. Fix it by writing or generating tests first, and committing them separately.
- Asking for a rewrite in one go. Large automated changes are almost impossible to review. Fix it by requesting one small change at a time, with tests between steps.
- Letting the tool change behavior along with structure. Agents sometimes "fix" things as they go. Fix it by stating that behavior must not change, and by reading every diff for altered conditions.
- Sending sensitive code without checking policy. Legacy systems often hold proprietary or regulated logic. Fix it by checking your policy, and consider options that use your own keys or self-hosting, such as Aider, Refact.ai or Potpie.
- Skipping human review on risky code. Automated checks miss business meaning. Fix it by having a developer who knows the domain review anything touching money, security or personal data.
How we put this together
This is an evidence-based guide built from each tool's listing in our directory, which is compiled from the vendor's own site and pricing page. It is not a review based on using the tools. We don't rank tools, and nobody paid to be included. Some outbound links may be affiliate links; see our disclosure.
Frequently asked questions
Is there a free way to refactor legacy code with AI?
Yes, within limits. Aider is open source with no subscription, though you pay your model provider. Potpie's Individual Pro tier is $0 per month with 50 requests, Refact.ai has a Free plan, Cursor has a free tier, and ContextStream's Starter plan is $0 per month. Check each plan's caps.
Can AI refactor code without breaking it?
Not reliably on its own. It can make useful structural changes, but it can also alter behavior without warning. Tests written before the change, small commits and human review are what keep a refactor safe.
Which tool should I use for a large migration?
That depends on your stack and policy. Claude Code lists migrations among its jobs, Diffblue Cover helps with tests for Java and Python, and Potpie and ContextStream help you map dependencies first. Try your chosen tool on one module before committing to a bigger effort.
Do I need tests first?
Yes, ideally. If the code has none, write tests that capture current behavior before you refactor. Without them you have no objective way to check the result.
Can I keep my code on my own infrastructure?
Some options allow it. Potpie can be self-hosted or deployed in your own VPC, Refact.ai is open source and can be self-hosted, and Aider works with local models such as those run through Ollama. Confirm the details with each vendor.
Compare more options in our Coding category. To make the result easier for the next developer, see how to document a codebase with AI.
