Your codebase changes faster than it used to, with coding agents turning around pull requests in minutes and teams shipping several times a day, and the cleanup is not keeping pace. Across 211 million changed lines of code, the share of refactored code fell from 25% in 2021 to under 10% in 2024, while copy-pasted code climbed from 8.3% to 12.3%.

That gap is why refactoring carries more weight now than it did a few years ago. Generate more code more quickly and the debt compounds at the same speed, and what has changed is that you can now point an agent at the problem, provided you keep the judgment calls in your own hands.

This guide covers what refactoring is, when it earns its place, the techniques worth knowing, and a workflow for letting AI help without breaking things. Instead of a flat list of techniques, you will see how a refactor unfolds in sequence.

What code refactoring is (and what it isn't)

Refactoring is changing the internal structure of code to make it easier to understand and cheaper to modify, without changing what the code does when it runs. Martin Fowler's canonical definition puts it as a change "made to the internal structure of software to make it easier to understand and cheaper to modify without changing its observable behavior." As a verb, it is restructuring software by applying a series of those small changes, again without altering observable behavior.

That phrase, without changing observable behavior, is the whole safety guarantee. Everything in the table below differs from refactoring on exactly that constraint.

| Activity | Changes structure? | Changes behavior? | | --- | --- | --- | | Refactoring | Yes | No | | Bug fixing | Maybe | Yes (corrects it) | | Adding features | Often | Yes (adds it) | | Rewriting | Yes | May deliberately change |

Fixing a bug moves the actual behavior toward the intended behavior, which is a behavior change, so it is not refactoring. Adding a feature gives you a new observable capability, so that is not refactoring either. Rewriting is its own category. J.B. Rainsberger draws the line cleanly between a refactor, which keeps the behavior of the current system, and a rewrite, which might change that behavior on purpose. Fowler is also careful to separate refactoring from general cleanup. Refactoring is a specific technique built on small, behavior-preserving transformations, and that precision lets the discipline make real promises about safety.

Why refactoring matters for code quality and long-term velocity

Technical debt raises the price of every future change you make. Global technical debt has reached an estimated 61 billion days of repair time, a 2025 figure its authors call conservative, and that cost shows up as maintenance work that competes directly with feature delivery. Refactoring attacks the cost head on, since being able to make changes more rapidly means shipping features faster and fixing problems sooner.

In 2026 there is a fresh reason to care about structure. Higher AI adoption brings teams both more throughput and more instability, and the instability lands hardest on poorly structured code. Test-driven development keeps AI's gains in place, and the more code you generate, the more refactoring discipline you need to keep it from rotting under you.

When to refactor code (and when to hold off)

Fowler treats refactoring as an opportunistic activity you handle during regular work rather than as a scheduled project, and a few patterns cover most of it:

• Preparatory refactoring: When a feature needs to land in code that isn't shaped for it, restructure first. Kent Beck's formulation is to first make the change easy, then make the easy change.

• Opportunistic cleanup: Follow the camp-site rule and leave code in a better state than you found it. Duplication is a useful trigger, because when you are about to repeat the same pattern a third time, the design is usually asking for a refactor.

• When to hold off: Refactor from a known baseline. Ideally the existing test suite is green; on legacy code, add characterization tests around the behavior you need to preserve before changing the structure. Deadlines, throwaway code, and rabbit-hole cleanups are signals to leave it alone. Refactor when the investment is likely to make upcoming changes faster or safer.

What code to refactor

Code smells are your first signal. Kent Beck coined the term code smell to mean a surface indication that usually points to a deeper problem, and a smell is a quick prompt to investigate rather than a verdict. Four families of smells account for most refactoring triggers:

• Long methods and large classes: Long methods are worth a second look when they combine multiple responsibilities, require extensive context to understand, or change frequently. Classes that accumulate too many fields and responsibilities also become harder to modify safely, because a small change can produce unexpected effects elsewhere.

• Duplicated code: Repeated logic across files or functions multiplies the chance of an inconsistent update later.

• Long parameter lists and tight coupling: Long parameter lists often hurt readability and can signal that a function or class is carrying too many responsibilities. Tight coupling tends to show up as message chains like $a->b()->c()->d(), where any change to the relationships breaks the caller.

• Dead code, unclear names, and magic numbers: Unused variables and unreachable branches are clutter, and a magic number is a bare literal like 1.05 whose meaning isn't obvious from context. The fix is symbolic constants with names that say what they mean.

None of these smells forces a refactor on its own, but each one raises the odds that the code under it will fight your next change.

Core code refactoring techniques every developer should know

These are the workhorses from Fowler's catalog and refactoring.guru, the moves you will reach for most often.

Extract Method breaks up long functions. You take a fragment that hangs together, move it into its own method, and replace the original code with a call.

// BEFORE

void printOwing() {

printBanner();

System.out.println("name: " + name);

System.out.println("amount: " + getOutstanding());

}

// AFTER

void printOwing() {

printBanner();

printDetails(getOutstanding());

}

void printDetails(double outstanding) {

System.out.println("name: " + name);

System.out.println("amount: " + outstanding);

}

Responsibility shuffles come next. When one class does the work of two, Extract Class pulls a cluster of fields and methods into a new class. Move Method relocates a method to the class that actually uses it most.

Rename Method fixes names that hide intent. When a name doesn't explain what the method does, you change it, and integrated development environment (IDE) automation finds and corrects every reference so the rename stays safe.

Inline Method removes needless indirection. When a method body is clearer than the method name wrapping it, you replace the call with the body and delete the method.

Replacing conditionals is the next common move. Guard clauses turn nested conditionals into early returns, which flattens the structure so the main logic isn't buried under indentation. When a conditional branches on object type, Replace Conditional with Polymorphism moves each branch into a subclass. Used together, Extract Method, Extract Class, and Move Method let you consolidate duplication by replacing the copies with calls to a single reusable unit.

Best practices for refactoring safely without breaking things

Discipline is what makes that safety guarantee real, and it comes down to a handful of habits:

• Cover the code with tests first: Tests are your primary evidence that behavior hasn't changed, and if you're changing them at the same time, or have none, your  confidence that behavior is unchanged is much lower. On legacy code, characterization tests capture what the code does today before you touch anything.

• Work in small, behavior-preserving steps: Running the test suite after each small step keeps the process predictable and catches problems while the change is still cheap to undo.

• Keep refactoring commits separate: Refactoring belongs in separate change lists from feature work, which keeps review tractable and lets you revert a risky feature change without losing the cleanup underneath it.

• Lean on automated IDE refactorings: IntelliJ IDEA ships automated actions for Rename, Extract Method, Inline, and Change Signature that correct every reference mechanically instead of leaving you to chase them by hand.

• Watch for the two classic traps: Scope creep turns a quick rename into restructuring three modules in the middle of a bug fix, and big-bang rewrites throw away refactoring's safety advantages unless they're broken into smaller, reversible steps.

Hold to these habits and refactoring becomes a more predictable and reviewable part of the work instead of a large opaque gamble. They also become the guardrails once you hand the work to an AI agent, which is where the loop below picks up.

How to refactor with AI agents: identify, decide, prep, execute

AI changes how much refactoring you can take on, but not the discipline it requires. When AI refactored less-healthy code, defect risk ran 30% higher across six LLMs. It multiplies whatever discipline you already have, so the safe way to run it is a loop with a human checkpoint in the middle: identify, decide, prep, and execute.

Identify: let an agent find where the refactor should happen

The first job is figuring out what to touch, and an agent can help because much of the signal lives in version-control history. Code churn is a well-studied defect signal. Hotspot analysis combines churn with complexity to find the risk magnets, the complex code that also changes often. An agent can mine that history at a scale you would not attempt by hand. It pulls the files that churn the most, the bug-fix commits that keep landing in the same place, and the changes that had to be reverted, then produces a ranked list of candidate hotspots for a developer to refactor.

Decide: a human picks what's actually worth doing

Once you have the ranked list, a person weighs which candidates are genuinely worth pursuing, and the agent's job stops at proposing them. The person approving a change carries the accountability for it, whichever agent surfaced the candidate. This is the checkpoint where a human stays firmly in the loop, and the checkpoint keeps an agent helping you rather than quietly making things worse.

Prep: the agent locks in current behavior with tests

Before anything moves, the agent helps build the safety net. It can generate characterization and regression tests around the current behavior, including edge cases it can infer from the code and existing test suite. Those tests still need review, since generated coverage is not proof that every important behavior has been captured. Completing that work before the refactor begins makes the next step safer.

Execute: the agent carries out the approved refactor

With the net in place, the agent applies the approved refactor in small diffs and runs the tests at each stage. The scope stays deliberately tight, and behavior changes stay in separate commits from structure changes, since mixing them makes the result much harder to validate.

Large-scale migrations show how far these workflows scale beyond individual refactors, and the economics come through clearly in real cases. One test migration at Airbnb moved nearly 3,500 React test files in six weeks against an original estimate of a year and a half. A Google migration of unique ID types ran 50% faster with AI authoring 80% of the changes. There's a detailed write-up on pushing this further with parallel agents on massive refactors. The same team has also published guidance on maintaining code quality in agent-generated changes.

Automating large-scale refactoring with OpenHands

Large refactors create a coordination problem. Changes may span many files or repositories, and parallel work has to be sequenced carefully to avoid unnecessary conflicts.OpenHands, is an open-source platform for building and running software engineering agents. Teams can use it to turn refactoring workflows into repeatable automations that can run locally, in the cloud, or within governed enterprise environments. It is not the refactoring tool itself but it provides the workflow, automation, and control layer around the agents doing the work.

It runs agents in the outer loop that write characterization tests, apply the refactor, run the suite, and open reviewable pull requests, and its primary interface, Agent Canvas, connects to Claude Code, OpenAI Codex, and Gemini CLI through the Agent Client Protocol (ACP) so you keep the tools you already use. For larger codebases, OpenHands can use dependency information to coordinate parallel work and reduce the likelihood of conflicting changes. OpenHands has demonstrated this approach in a published COBOL-to-Java modernization workflow .  There is also a broader playbook for modernizing legacy systems.

Because the MIT-licensed core is open source, teams with stricter infrastructure requirements can run OpenHands on their own infrastructure. Keeping the source code and related data within a defined boundary still depends on the model, integrations, logging, and telemetry configured for the environment.  OpenHands Enterprise adds governance capabilities such as scoped access, auditability, and centralized visibility into agent activity. The OpenHands team has also used its own agents on the OpenHands codebase. In October 2024, the team reported that its GitHub resolver had authored or co-authored- 37% of recent commits to its own codebase.

Make refactoring a continuous habit

Refactoring works best as a regular part of programming rather than a quarterly cleanup sprint. Done well, it stops being a special line in the project plan and becomes part of how code gets written each day. Smaller scopes, higher frequency, and reversible steps beat the big-bang batch every time.

The practical version for a team is to reserve a small, steady slice of attention for refactoring, sized to the change in front of you. AI agents can extend that habit past what one developer can sustain by hand, running repeatable hotspot-analysis and refactoring workflows, generating tests before changes, and routing work to a human at defined review points. The discipline that keeps the work safe stays the same at any scale. Pick one hotspot from your commit history, run your first refactor locally with an agent behind a green test suite, and take the same workflow to cloud or self-hosted infrastructure when your team is ready.

Frequently asked questions about code refactoring

What is a simple example of code refactoring?

Extract Method is the canonical example because the whole change stays local. You pull a fragment out of a long function without touching any caller, so the blast radius is one file and the existing tests still cover it. Agents are often useful for this kind of mechanical transformation, especially when the test suite provides a clear validation loop. OpenHands docs walk through pointing one at your own code.

How do you decide what to refactor first?

Fowler's activity-based heuristic is the standard answer, where crufty but stable areas can be left alone and high-activity areas need a low tolerance for cruft. In practice you combine file churn with complexity to find the hotspots, and an agent workflow on OpenHands can analyze repository history and rank candidate hotspots for review.

Is it safe to let AI refactor production code?

It can be made safer with tightly scoped changes, characterization tests on legacy paths, mandatory human review, and independent security and CI checks. Reviewing AI-generated code takes more effort than human code for 38% of developers, which is exactly why the human stays in the loop. OpenHands gives you a few ways to get started on a low-stakes refactor before you point an agent at code you care about.

How often should a team refactor?

A workable cadence has three layers. Cleanup rides along with every pull request you were already writing, and a scheduled hotspot sweep, monthly or quarterly, works through the files your history flags. Large coordinated migrations are the rare third layer, and they are where an agent-driven approach starts to pay off.

About OpenHands

OpenHands is the open-source platform for building and running AI coding agents, with the interface, automations, and control layer needed to go from a single local agent to a system running across an entire organization. The mission is to make agent-based software development accessible, transparent, and controllable by default. That starts in the open. The core framework is open source, giving developers and platform teams full visibility into how agents execute work and interact with their systems. The project has over 80,000 GitHub stars, over 9 million downloads, and contributions from hundreds of developers. OpenHands is used by engineers at large enterprises and fast-growing startups to build, run, and scale AI coding agents across real software engineering workflows. The long-term vision is to become the full stack AI coding agent platform for software engineering. Not just helping developers write code, but running meaningful parts of the software lifecycle.