I love a good refactor. Who doesn’t? Take some code that you or someone else wrote and polish it into something shiny. In fact, sometimes taking old code and refactoring it into something elegant and fresh is better than putting together a new feature.

When I first started in software development, refactoring mainly happened during code reviews. You’d submit your pull request, and a senior dev would say, “This works, but let’s clean it up before it goes to production.” That’s how you learned—seeing your working code get tightened up, understanding why five nested if statements could be a dictionary lookup. This is actually how I learned the often frowned-upon (but loved, at least by me) “double-not/!!” operator in JavaScript to turn a value into a hacky boolean. As a junior and intermediate developer, that feedback loop made you better.

Refactoring is one of those things every developer knows they should do more often, but it’s also the first thing that gets pushed to “next sprint” when deadlines loom. Unfortunately, though, bad code doesn’t just sit there; it compounds. What starts as a shortcut becomes technical debt.

Luckily, since I started writing code almost 15 years ago, the tooling has evolved dramatically. In the last 2 years, this whole subject seems to have flipped upside-down. IDEs got smarter with built-in refactoring. Linters caught code smells. Now, AI-powered tools can handle complex, multi-file refactoring that used to take days in a few minutes. There’s quite a bit to cover on this subject, so let me walk you through what refactoring actually means, why it matters, and which tools handle different scenarios best.

Introduction to Code Refactoring

I likely view “refactoring” through a broader lens than some folks do. So that we are all on the same page, let’s get the definition out of the way: refactoring is restructuring existing code without changing its external behavior. You’re making the code cleaner, more maintainable, or more efficient—but from the outside, it does exactly the same thing it did before. This is the textbook definition, but there is a bit more to it.

In practice, refactoring means two different things depending on where you are in your career and what you’re working on.

The first type is incremental refactoring—you write code that works, ship it, and then come back to make it more elegant and resilient. Maybe you extract a function, consolidate some duplication, or improve error handling. This is the refactoring that happens in code reviews, where someone says, “This works, but here’s how we can make it better.” It’s usually contained to a module or a few related files.

The second type is modernization refactoring—rewriting legacy code to fit a new paradigm, language, or technology. This is what happens when you’re migrating mainframe code exposed via SOAP to RESTful APIs (been there, done that too many times), converting a monolith to microservices, or updating a codebase from Python 2 to Python 3. This is system-wide work that touches dozens or hundreds of files, and it’s where things get risky.

In both cases, the true goal is not adding features. You’re not fixing bugs (well, sometimes you find them, but that’s not the goal). You’re improving the internal structure so the code is easier to work with tomorrow, next month, or when someone else inherits your codebase.

Martin Fowler wrote the book on this—literally, it’s called “Refactoring”—and his definition still holds up: “a disciplined technique for restructuring an existing body of code, altering its internal structure without changing its external behavior.” That discipline part matters because cowboy refactoring without tests is how you ship broken code while feeling productive.

Benefits of Code Refactoring

If you’ve ever been knee-deep in a project that works but is an absolute disaster in terms of code quality, then you know: Code that works isn’t the same as good code. When you refactor to improve code, you get quite a few benefits, which include:

Maintainability: When you need to add a feature six months from now, you want organized code with clear responsibilities. Refactored code means less time figuring out what’s happening and more time making changes.

Fewer bugs: Duplicate code means duplicate bugs. Consolidate logic; you have fewer places for things to go wrong.

Better performance: When you restructure, you spot inefficiencies—unnecessary loops, redundant API calls, poor data structures.

Easier onboarding: New developers can read refactored code and understand it faster.

Compounds over time: Good code stays good longer. Bad code gets worse faster. Small, consistent costs beat massive ones when everything breaks.

So, why isn’t everyone refactoring all of the time?! The catch: Refactoring takes time upfront. That’s why teams skip it. The actual business value is a little harder to convey compared to building new features. But developers and leaders in tech know that bad code quickly halts velocity. The good news is that we live in a time where AI tools can compress the timelines for these tasks significantly, making refactoring realistic even when you’re under pressure and time constraints. All you need is the right tool, or even multiple tools.

Best Code Refactoring Tools

The ecosystem of refactoring tools has evolved dramatically in the past few years, especially with AI entering the picture. Let’s break down what’s out there.

Traditional IDE refactoring tools have been around forever—IntelliJ, VS Code, Eclipse—they all have built-in support for basic refactoring operations. Rename variable, extract method, move class. These work great for local, well-defined changes. The limitation? They’re mechanical. They can’t reason about your codebase as a system. You need to do everything still by hand, but with a little less effort than doing it completely manually.

Linting and static analysis tools like ESLint, Pylint, or SonarQube detect code smells (I hate this term, but for some reason the industry still uses it) and suggest fixes. They’ll yell at you for complexity, duplication, or style violations. Useful for enforcing standards, but they won’t actually do the refactoring for you—they just tell you what’s wrong.

AI coding assistants like GitHub Copilot and Cursor can help with code suggestions and explanations. They’re good at generating code and answering questions, but they’re more useful as assistants, not agents, when it comes to large codebases and complex tasks. For the most part, you’re still driving; they’re just helping. Similar to Tesla’s FSD tech, pretty good most of the time, but you still want your hands near the wheel.

Then there’s Sourcegraph Amp, which sits in a different category than the others. It’s not an assistant—it’s an agentic coding tool. The difference? You can tell Amp, “Refactor the authentication error handling across all our API routes,” and it will create a plan, make the changes, and run tests. Other platforms like Cursor and Windsurf can do this as well, but Amp is truly enterprise-ready and built to handle complex, multi-file refactoring tasks autonomously.

Amp runs on Claude Sonnet 4.5 as its main model, with up to 1 million tokens of context. That means it can see and reason about your entire codebase, not just the file you have open. And when it needs deeper analysis—like figuring out system-wide implications of a refactoring—it can consult “the Oracle” (currently GPT-5), which excels at complex reasoning tasks.

What makes Amp particularly effective for refactoring is its subagent system. When you have a large refactoring task, Amp can spawn multiple subagents to work in parallel—one handling API routes, another updating tests, another checking documentation. Each subagent has its own context window and reports back when done, keeping your main thread clean.

From the command line, refactoring with Amp looks like this:

amp -x "Analyze these files for code duplication, suggest refactoring opportunities, and estimate the impact of proposed changes"

Or for something more complex:

amp -x "Use Amp Oracle to create a comprehensive refactoring plan for this codebase, including architectural debt assessment, effort estimation, and risk analysis"

That’s the kind of work that used to take hours of manual analysis. Now it happens in minutes, and you get a detailed plan before any code changes. Then you can let Amp spin off a bunch of sub-agents to implement everything.

Each of these tools has a place.

Code Smells and Legacy Code

As I mentioned before, I really can’t stand this term, but I’m going to use it here anyway! Code smells are warning signs that your code needs refactoring. Not bugs—your code runs—but indicators that something’s off (AKA something is stanky). The types of things you want to pick up and most tools focus on here are:

Duplicate code: Same logic in multiple places. Change one, forget another, introduce a bug.

Long methods: Functions doing too many things. Hard to test, understand, and modify.

Large classes: The “God object” that knows everything and does everything.

Long parameter lists: Functions taking eight arguments are trying to be too flexible.

Divergent change: Modifying the same class for unrelated reasons.

Shotgun surgery: Single change requires updates everywhere.

How different tools handle code smells

For simple, localized smells, IDE refactoring tools excel. IntelliJ will spot a long method and let you extract it in seconds—select the code, hit the refactoring shortcut, name the new method. Done. VS Code with the right extensions does similar work for JavaScript/TypeScript. These are perfect for the code review refactoring we talked about earlier.

SonarQube takes a different approach—it identifies the smells across your entire codebase and gives you metrics. Here’s your complexity score, here’s your duplication percentage, and here are the files that need attention. It won’t fix anything, but it tells you where to look.

Legacy code—code without tests, code written by someone who left five years ago—is where these smells concentrate. At the senior level, legacy refactoring often becomes part of modernization efforts. You’re migrating mainframe systems to REST APIs, converting procedural code to object-oriented patterns, and updating frameworks untouched for a decade. This is where the stakes get higher and the scope gets wider.

This is where AI agents like Amp change the equation. Before touching anything, you can get a comprehensive analysis:

Ask the Oracle to analyze this module and identify code smells, duplicate patterns, and suggest a refactoring approach that minimizes risk.

The Oracle provides a detailed analysis of dependencies, potential breaking points, and a phased approach. For duplicate notification logic spread across channels (email, SMS, push):

Analyze how sendEmailNotification and sendSMSNotification are used throughout the codebase. Then work with the oracle to figure out how we can refactor the duplication between them while keeping changes backwards compatible.

Amp searches your code, understands usage patterns, consults the Oracle for strategic planning, and then proposes a refactoring plan that extracts common logic without breaking functionality.

Automate Code Refactoring with AI Tools

Refactoring used to be entirely manual. You’d open a file, read the code, make changes, update tests, and hope you didn’t break something three layers deep. For incremental refactoring, this was manageable. For system-wide refactoring—migrating patterns across dozens of files—it was brutal.

The shift to AI-assisted refactoring has changed the game in the past couple of years. Early attempts were messy—AI would suggest changes that looked right but broke subtle dependencies. Modern agents like Amp have gotten significantly better, with high success rates on complex refactoring tasks.

The key difference: AI agents can understand existing functionality across your entire codebase, encapsulate that understanding (either in context or through generated tests), then refactor en masse while maintaining behavioral consistency. You’re no longer limited to one module at a time.

Where different tools excel

GitHub Copilot and Cursor are great for quick, interactive refactoring. You highlight a 50-line function, ask “extract the validation logic,” and it suggests the extraction immediately. Fast feedback loop, perfect when you’re already in the flow. But they work at the file level—trying to coordinate changes across 20 files means 20 separate interactions.

Traditional IDE refactoring (IntelliJ, PyCharm) handles mechanical transformations reliably. Rename a variable across your entire project? It’ll find every reference, including string literals if you want. Move a class? It updates all imports. These are safe operations because the IDE understands the language’s syntax tree.

AI agents like Amp handle complex, multi-file refactoring where you need reasoning about system behavior. Say you’re updating API error handling across 20 route handlers. Manually, that’s hours of reading context, making consistent changes, and updating tests. With Amp:

Use subagents to refactor our API error handling. Assign different route groups to different subagents so they can work in parallel.

Each subagent has its own context window and works independently, preventing the context contamination that happens when you’re juggling multiple complex changes.

For pattern recognition, AI spots repeated patterns you might miss—logic implemented slightly differently across files:

amp -x "Analyze all .js and .ts files in the current directory for code duplication and suggest refactoring opportunities"

Dependency analysis before making changes prevents “change one thing, break five others”:

I need to refactor our database query layer to use a connection pool. Use the oracle to analyze the current implementation and identify all the places that will need updates, considering both direct dependencies and indirect effects.

What AI still can’t do well

Understanding the business logic context, not in the code. If there’s a hack because of a client requirement from three years ago, AI won’t know unless it’s documented.

Making judgment calls about what “better” means for your situation. Is readability more important than performance? Depends on your team.

Knowing when not to refactor. Sometimes legacy code is scary but stable, and touching it is riskier than leaving it alone.

Code Optimization and Code Duplication

Code duplication is the most common refactoring target. But not all duplication is bad, and not all optimization is worth it.

When to refactor duplication

If you’re changing the same logic in multiple places when requirements change, eliminate the duplication. DRY (Don’t Repeat Yourself) matters when repeated code represents the same business rule.

When duplication is fine

Sometimes copied code serves different purposes that look similar now but will diverge later. Sometimes, abstraction overhead isn’t worth it for three lines of code.

Different approaches to handling duplication

ESLint or Pylint will flag duplicate code blocks and yell at you. SonarQube gives you a duplication percentage and tells you which files are the worst offenders. Useful for knowing where to focus, but you still do the work manually.

With AI agents like Amp, you can identify and plan duplication removal strategically:

1. Analyze the duplication:

Find all instances where we're manually validating user input across our API endpoints. I want to understand the patterns before consolidating.

1. Consult the Oracle for a refactoring plan:

Based on the validation patterns you found, ask the oracle to design a refactoring approach that consolidates this logic while maintaining backwards compatibility.

1. Implement the plan:

Please implement the refactoring plan that the Oracle outlined. Proceed with each phase, and make sure the implementation at each phase works as expected.

Code optimization through refactoring

Refactoring often reveals performance issues. When you consolidate logic, you spot inefficient patterns—N+1 queries, unnecessary loops, data fetched but never used.

IntelliJ will warn you about unused variables and inefficient patterns. Static analysis tools catch obvious performance issues. But understanding system-wide performance implications requires deeper analysis, which can be done with Amp like this:

Analyze our database query patterns in the user service. Use the oracle to identify performance bottlenecks and suggest refactoring approaches that would improve query efficiency.

The key: optimization should be measured. Refactor for structure first, optimize when you have data showing it’s necessary.

Code Readability and Best Practices

Readable code is code you can understand six months from now. Refactoring for readability is often more valuable than refactoring for performance.

What makes code readable

Clear naming. Functions and variables describe what they do, not how. getUserEmailAddress() beats doTheThing().

Appropriate abstraction levels. Functions do one thing at one level. Don’t mix high-level business logic with low-level data manipulation.

Consistent patterns. Handle errors the same way across modules. Consistency reduces cognitive load.

Documentation where it matters. Good code is self-documenting most of the time, but complex decisions and edge cases need comments.

How different tools improve readability

Prettier and ESLint enforce consistent formatting automatically. Same indentation, same quote style, same spacing. Removes bikeshedding from code reviews.

IDEs like PyCharm suggest better variable names based on type and usage. They’ll also warn when functions get too complex—if your cyclomatic complexity hits 15, you’ll see a warning.

For larger readability refactoring, you can use Amp’s AI agents to analyze and suggest structural improvements:

Review this module for readability issues. Suggest better function names, identify overly complex logic, and recommend how to break down large functions.

For controllers that have grown too large, a prompt may look like this:

This controller has grown too large and handles too many responsibilities. Use the oracle to propose a refactoring that separates concerns while maintaining the current API contract.

Best practices when refactoring:

Work in a branch. Always. Even with AI doing the work.

Start with tests. If you don’t have tests, write them first. You can get Amp to do this with a prompt like this:

Generate comprehensive unit tests for this module before we refactor it. I want to ensure we don't break existing behavior.

Review AI-generated changes carefully. Treat it like a code review—check logic, verify tests pass, ensure changes align with what you intended.

Use analysis tools for planning, not just implementation. This is where Amp’s Oracle can come in to help:

Before we make any changes, use the oracle to analyze the impact of refactoring this authentication layer. What are the risks? What dependencies exist? What should we test?

Conclusion

Refactoring isn’t optional—it’s maintenance. You can defer it, but technical debt compounds. The difference now is that tools have changed the economics of refactoring. What used to take days can happen in hours.

The right tool depends on the job:

Quick fixes while coding? Use your IDE or Copilot.

Identifying what needs refactoring? Use traditional tools like SonarQube, ESLint, or newer tools like Amp’s Oracle.

Complex, multi-file refactoring? AI agents like Amp.

Mechanical transformations? Traditional IDE refactoring is still the safest bet.

But tools don’t fix bad processes. If your team doesn’t value refactoring, if you don’t have tests, if you ship features without considering maintainability, better tools won’t save you. They’ll just help you create technical debt faster. Set aside time regularly to clean up code. Write tests. Review changes carefully—whether you wrote them or AI did. The goal isn’t perfect code, it’s code you can maintain and extend without pain.