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Best Practices for Refactoring Legacy Code

Refactoring legacy code can feel overwhelming, but it's essential for improving code quality and reducing technical debt. Legacy code - often defined as code without tests - creates risks like hidden bugs, slow feature development, and security vulnerabilities. Refactoring focuses on small, incremental changes to improve internal structure without altering functionality. Here's what you need to know:

  • Start with a safety net: Write characterization tests to document current behavior and catch regressions.
  • Prioritize fixes: Focus on high-impact areas like frequently changed or error-prone modules.
  • Refactor incrementally: Use techniques like the Strangler Fig pattern or branch-by-abstraction to avoid large-scale rewrites.
  • Tackle dependencies: Simplify tightly coupled code with methods like dependency injection or extracting interfaces.
  • Document and test: Keep documentation up to date and ensure robust test coverage to maintain improvements.

Refactoring isn't a one-time task - it’s an ongoing process that keeps your codebase stable and easier to maintain while minimizing risks. By following structured techniques and focusing on small, manageable steps, you can transform even the messiest legacy systems into something far more reliable and efficient.

Building a Safety Net Before You Start Refactoring

Establishing a solid safety net is essential when tackling technical debt and preparing for refactoring. This step ensures errors are caught early and the process remains manageable. As Gareth Clubb from Codably.dev aptly states:
"You cannot preserve behaviour you cannot observe." [2]
The cornerstone of this safety net is your test suite. With the right tests in place, you can turn refactoring into a structured, predictable process instead of a risky endeavor.

Automated Testing and Regression Coverage

When dealing with legacy systems, start by writing tests that capture the system's actual behavior. This method, known as characterization testing, helps document what the code currently does, ensuring any unintended changes trigger immediate test failures [2][5].
In legacy environments, integration tests often prove more effective than unit tests. Since components in older systems tend to be tightly interconnected, issues are more likely to arise in the interactions between modules rather than within individual functions. A mocked unit test might pass, even as the overall system fails silently [5].
Here’s a good starting point:
  • Write a test for each bug you encounter to prevent it from recurring [5].
  • Gradually build test coverage and set up a continuous integration pipeline with a --fail-under threshold. This ensures your coverage doesn’t drop, even if it starts low. Aim for at least 80% coverage on core business logic before diving into significant refactoring [6].
Be cautious with AI-generated tests, as research shows 40–50% of them can be unreliable [5]. These tests often mock every dependency, which can lead to false positives. Always review AI-generated tests thoroughly to maintain their quality and usefulness.
Once your tests are in place and the current behavior is secured, move on to documenting the system to uncover hidden dependencies.

Documenting Baseline System Behavior

Before making structural changes, take the time to monitor your system’s entry points - such as APIs, cron jobs, and background workers - and log all calls to external dependencies. This runtime mapping can be eye-opening. For instance, one modernization effort revealed that 3 of the top 5 API endpoints, which handled $2.3 million in monthly transactions, were undocumented [6].
For outputs that are particularly complex - like large JSON responses, generated HTML, or detailed reports - use approval testing. This method captures the entire output once and compares future runs against it. It’s a practical alternative to writing hundreds of manual assertions, which can be both tedious and error-prone [2][7].
One important guideline: don’t mix bug fixes with documentation. If a characterization test reveals behavior that seems incorrect, document it as-is and create a separate ticket for the bug. Combining bug fixes with refactoring makes it harder to pinpoint the source of new regressions [2][4].

Setting Realistic Goals and Priorities for Refactoring

With testing and documentation in place, the next step is honing in on the areas of your codebase that will yield the most improvement. Legacy systems are often sprawling, and attempting to overhaul everything at once will likely lead to burnout and missed deadlines. The smart move? Focus on the areas where your efforts will have the greatest impact.
"Technical debt isn't 'bad code.' It's the delta between the current state of your system and the state it needs to be in to support your current and near-future business goals." - CodeIntelligently [9]

Identifying High-Priority Areas

A good starting point is hotspot analysis, which pinpoints sections of code that are both complex and frequently changed. For example, a messy module that rarely gets touched might not pose much risk, but one that's updated every sprint can become a serious liability [9][10].
Two key metrics can help you identify these hotspots:
  • Delivery impact: This is calculated as (actual time – estimated time) / estimated time. If a module consistently takes more than double the estimated time to complete, it’s a sign that the code needs attention [9].
  • Change failure rate: If approximately 30% or more of changes to a specific module result in production issues, that module is costing the business money and productivity [9].
Another valuable signal comes directly from your team. Ask your developers which parts of the codebase they avoid modifying. Their hesitation often stems from real experiences with fragile or poorly understood code [10].
Once you’ve identified these hotspots, assess their impact on the business. This step ensures you’re prioritizing the work that truly matters.

Balancing Technical Debt Against Business Needs

After pinpointing the high-impact areas, an impact-effort matrix can help you categorize refactoring tasks. This framework divides tasks into four groups:
  • High-impact, low-effort: Tackle these immediately.
  • High-impact, high-effort: Plan these for the next quarter.
  • Low-impact, low-effort: Address these only when you’re already working in that part of the code.
  • Low-impact, high-effort: These tasks are often best left untouched [9][8].
To gain leadership support, it’s crucial to frame technical issues in financial terms. Instead of saying a module has high cyclomatic complexity, explain that it adds $180,000 in engineering costs per quarter due to delays and rework [9][10]. This approach makes it easier to prioritize refactoring tasks.
For example, data-driven goals tied to measurable outcomes can make a compelling case. Consider the example of raising test coverage from 20% to over 60% across multiple repositories in less than a year. Achievements like this turn refactoring into a funded priority rather than a wishlist item [3].
Source: CodeIntelligently [9]

How to Refactor in Small, Controlled Steps

After setting your priorities, the next step is to refactor in small, deliberate steps. It's tempting to tackle everything at once, but large-scale changes often backfire. They can freeze feature development for months, introduce hard-to-find bugs, and sometimes even leave the codebase in worse shape. By taking an incremental approach, you can lean on your testing and documentation to reduce risks.
"Refactoring is a controlled technique for improving the design of an existing code base. Its essence is applying a series of small behavior-preserving transformations... However, the cumulative effect... is quite significant." - Martin Fowler [10]
This quote highlights the power of gradual change: while each step might seem minor, the combined effect leads to meaningful improvements.

Breaking Changes Into Smaller Units

The idea is simple: keep changes small enough to test, review, and revert in minutes - not days. A good rule of thumb is to aim for a refactoring loop that takes between 5 and 15 minutes. If you find yourself working for an hour without committing, it's time to break the task into smaller pieces [2].
Here are a couple of key practices to follow:
  • Separate refactoring from feature development. Each commit should either maintain existing behavior or introduce a change - never both at the same time [2].
  • Use feature flags. Gradually roll out refactored code to a small percentage of users, increasing traffic as confidence grows. Always have a rollback option ready in case something goes wrong [14][15].
Another helpful technique is branch-by-abstraction. This involves adding a stable interface in front of legacy code and gradually replacing the implementation behind it. This allows the old and new code to coexist safely during the transition [15].
For larger-scale changes that impact entire components, the Strangler Fig pattern is a highly effective strategy.

Using the Strangler Pattern

When dealing with significant legacy systems, the Strangler Fig pattern is a great choice. Inspired by a tropical vine that overtakes its host tree, this approach involves building new functionality around the edges of the legacy system. Over time, the old code is replaced piece by piece [12][16].
A real-world example of this approach comes from Shopify. In March 2020, their Kernel Architecture Patterns team refactored the massive "Shop" model - a God Object with over 3,000 lines of code. Instead of rewriting everything at once, they followed a step-by-step process:
  1. Defined a new interface.
  2. Redirected calls to this interface.
  3. Created a new database table.
  4. Implemented double-writes to sync data between old and new systems.
  5. Backfilled existing data into the new table.
  6. Switched reads to the new source.
  7. Finally, deleted the legacy code.
This migration was completed without any downtime, even with over 1 million merchants relying on the system [13].
The key to this process is using a facade or indirection layer. This proxy routes requests to either the legacy system or the new implementation, ensuring that end users never notice the transition. As Martin Fowler explains:
"The alternative that my colleagues and I prefer, is to do a gradual process of modernization. Like the fig, it begins with small additions, often new features, that are built on top of, yet separate to the legacy code base." [12]
If you can't revert to the old system quickly, you're not fully leveraging the Strangler Fig pattern.

Practical Techniques for Refactoring Legacy Code

Once you've established a safety net and set clear priorities, it's time to dive into practical approaches for tackling legacy code. These methods focus on addressing three common problems: tangled dependencies, bloated logic, and code that's difficult to understand.

Breaking Code Dependencies

One of the biggest hurdles in legacy systems is tightly coupled code. When a class directly creates its own dependencies, like database connections, or relies on global singletons, testing becomes nearly impossible. Worse, making changes risks triggering a domino effect of failures.
The solution starts with finding seams - places where you can alter behavior without modifying the code itself. These could be dependency injection points, function parameters, or subclass overrides. Once identified, you can use test doubles (like mocks or fakes) to isolate the code for testing [2][1].
From there, specific patterns can help untangle dependencies:
  • Extract Interface: Swap a concrete implementation for an abstraction, allowing the rest of the system to rely on a contract instead of a specific class.
  • Sprout Method: When adding new functionality to messy, untested methods, write the new logic in a separate, testable method and call it from the legacy code.
  • Wrap Method: Useful for making non-testable classes testable by wrapping them in a layer that can be controlled more easily.
Here's a quick comparison of these techniques:
Addressing dependencies is just the first step. Next, you'll want to simplify overly complex code.

Simplifying Complex Code

Large, unwieldy methods are a common pain point in legacy systems. Their complexity can make developers hesitate to refactor, perpetuating the problem. The key is to break them down into smaller, more manageable pieces.
Kent Beck's advice captures this perfectly:
"Make the change easy, then make the easy change." [17][10]
To put this into action, consider techniques like Extract Function and Replace Conditional with Polymorphism.
  • Extract Function: Move a coherent block of logic into its own named method, making the purpose of the code clearer.
  • Replace Conditional with Polymorphism: When you see repetitive type-switch logic in multiple places, replace it with polymorphism to simplify and make the system easier to extend.
  • Split Phase: If a function is doing two unrelated tasks, divide it into two separate steps, each easier to test and maintain.
Focusing on areas with high churn - code frequently touched or modified - can yield significant improvements. This approach reduces bug-fixing time and helps maintain momentum during sprints.
Once you've streamlined complex logic, the next hurdle is deciphering opaque, hard-to-follow code.

Scratch Refactoring to Understand Opaque Code

Some legacy code is so tangled that it's nearly impossible to understand. No documentation, no tests, and years of accumulated business rules make it a risky proposition to refactor directly. This is where scratch refactoring comes in.
The concept is simple: temporarily refactor the code to understand its structure, then throw away the changes. This isn't about creating production-ready code. Instead, focus on renaming variables, extracting small functions, and tracing requests end-to-end to reveal how the code actually works. Once you have a clear understanding, you can start a proper refactor with tests [2][7].
One critical rule during scratch refactoring: don't fix bugs as you find them. Log any issues separately. Mixing bug fixes with structural changes can make the code even harder to work with in the long run [2][4].
These methods set the stage for testing and safely releasing your refactored code, which will be explored in the next section.

Testing and Releasing Refactored Code Safely

Once you've refactored your code incrementally and carefully, the next hurdle is deploying it without causing disruptions. To achieve this, you need a strong review and testing process before shipping the code, along with a deployment strategy that minimizes risk if something goes wrong.

Code Reviews and Continuous Integration

A reliable way to prevent regressions is by maintaining a robust continuous integration (CI) pipeline. This pipeline should automatically run all layers of tests for every commit. Additionally, tools like SonarQube or ESLint can be integrated to catch issues like rising cyclomatic complexity or code smells, ensuring your refactoring efforts genuinely improve code quality.
One important rule: Never mix refactoring with feature development or bug fixes in the same pull request. Keeping these changes separate makes it easier to pinpoint the cause of any regression, especially when using tools like git bisect to trace issues.
Peer reviews are another key part of the process, as they can catch edge cases that might have been missed. For larger architectural changes, implementing a Request for Comments (RFC) process can be helpful. It allows the team to discuss and address potential concerns early, when they are easier and cheaper to fix.
These practices create a strong foundation for deploying refactored code in a controlled and gradual manner.

Staged Deployments and Rollback Plans

Even the most thoroughly tested code can behave unexpectedly in production. A staged rollout strategy helps identify problems before they affect all users. One popular method is deploying the refactored code behind feature flags. Initially, the code remains inactive, and traffic is gradually shifted using expanding rings: starting with a canary release (1–5%), moving to a beta phase (10–25%), then scaling to 50%, and finally to 100% [18].
Progress through each stage only when specific metrics, such as error rate increases (+0.5%) or latency spikes (+20% p99), stay within acceptable limits. Feature flags offer a quick way to revert changes if needed. As Gareth Clubb aptly noted:
"If you cannot toggle back to the old path in seconds, you are doing a rewrite with extra steps, not a strangler fig." [2]
For critical operations like payment systems or data migrations, consider running the new implementation in shadow mode. This technique involves executing the refactored code in parallel with the legacy system, comparing their outputs without exposing the new behavior to users. This ensures correctness before rolling it out publicly [6].

Keeping the Codebase Clean After Refactoring

Refactoring is just the first step. Without consistent practices, even a freshly cleaned codebase can quickly fall back into chaos. The aim is to make a clean codebase the norm, not a fleeting achievement. This requires ongoing effort, starting with keeping documentation accurate and up to date.

Keeping Documentation Up to Date

Once refactoring is complete, it's crucial to update documentation continuously. Even minor changes should be reflected to avoid knowledge gaps. Studies reveal that when documentation is outdated, developers can spend up to 50% of their project time trying to understand unclear code [8].
A good strategy is to treat documentation like code. Using integrated tools that flag documentation changes during pull requests can help. For example, some systems block merges if documentation isn't updated or if documentation coverage drops. Pairing this with CI/CD enforcement ensures consistent updates. For broader architectural decisions, tools like Confluence, Notion, or GitHub Wikis are excellent for explaining the why behind major changes, while the how details can stay in the repository alongside the code [8].
Adopting the Boy Scout Rule is another practical approach: every time a developer works on a module, they leave the documentation slightly better than they found it [19][4]. These small, consistent updates add up over time.

Removing Dead Code

Dead code - often 10–20% of production codebases - clutters the code and can slow down builds or create subtle bugs if accidentally executed [20][22]. Similarly, 5–15% of npm dependencies in projects may go unused [20].
The best way to tackle dead code is through small, targeted pull requests instead of overwhelming, large-scale removals. Tools like Knip (for identifying unused exports and dependencies), ts-prune (for TypeScript), and Depcheck (for npm packages) can quickly highlight what’s safe to remove [20]. Before deleting anything, use git log to check its history - some rarely modified code might still serve a critical function, like generating an annual report [2]. For higher-risk code, consider commenting it out first, running all tests, and monitoring staging for a few days before fully deleting it [21].
"Deleting code is more dangerous than writing it. A wrong deletion causes a production outage." - CodeIntelligently [20]
Equally important is ensuring technical debt doesn’t creep back in.

Preventing Technical Debt From Building Up Again

To maintain a clean codebase after refactoring, you need consistent vigilance throughout the development process. Treat refactoring as an ongoing task by allocating about 20% of each sprint to maintenance and cleanup [4][19]. Regular sprints, CI/CD checks, and standardized practices help preserve the benefits of refactoring. For example, configure your CI/CD pipeline to block pull requests introducing code smells, unused variables, or broken documentation links [19][22]. Using standardized architectural patterns, often called "Golden Paths", can also prevent developers from solving the same problems in conflicting ways [22].
"The real test of a refactor is whether the next person who touches the module has an easier time than you did." - Gareth Clubb, Codably [2]
Finally, don’t ignore TODO, FIXME, and HACK comments. Regularly review them - if they’re still relevant, turn them into tracked tickets; if not, delete them. This simple habit helps keep the codebase honest and manageable [22].

How iDelsoft Can Support Your Refactoring Projects

Refactoring legacy code can be a daunting task, especially when outdated systems are tangled with modern technologies. To tackle this, having engineers who specialize in addressing these challenges can be a game-changer. That’s where iDelsoft steps in, helping businesses build dedicated remote engineering teams to handle complex modernization projects.

Access to Pre-Vetted Remote Engineering Talent

Legacy systems often combine a mix of programming languages, frameworks, and infrastructure choices that many developers don’t encounter regularly. iDelsoft provides access to pre-vetted engineering professionals skilled in more than 150 technologies, equipped to handle everything from aging monoliths to modern service architectures. This expertise is crucial for tackling issues like tight coupling and dependencies that can slow down refactoring efforts.
The importance of skilled engineers becomes even clearer when considering the rise of AI-assisted code. With 42% of committed code now AI-generated - and only 48% of AI suggestions consistently verified - experienced engineers are vital to identifying potential vulnerabilities [23]. Roman Labish, CTO, highlights the need for clarity in code reviews:
"If a reviewer can't explain what changed in under a minute, the PR is too big." [23]
iDelsoft’s engineers excel at identifying subtle issues, such as silent behavioral drift that can arise from AI-assisted changes, ensuring safer and more reliable refactoring - especially for systems lacking adequate test coverage.

Flexible Engagement Models

Every refactoring project is different. Some teams may need a quick audit of fragile modules, while others might require a long-term partner for a full-scale modernization effort. iDelsoft offers flexible engagement options to meet these varying needs. For teams unsure where to begin, discovery sprints can help evaluate system architecture and outline a modernization roadmap before committing to a larger project.
For extensive refactoring, iDelsoft can assemble dedicated teams - including tech leads and QA managers - that take full responsibility for the process. This allows in-house teams to stay focused on core products. Teams can be formed in as little as 72 hours, with potential cost savings of up to 60% compared to equivalent U.S.-based salaries.

Expertise in Legacy Systems and AI Development

Refactoring isn’t just about cleaning up code - it’s about introducing changes safely into systems that weren’t designed for easy updates. iDelsoft’s engineers bring deep expertise in techniques like creating Seams (entry points for altering system behavior without touching the source code) and writing characterization tests to lock in existing functionality before making changes [24][2].
In addition to legacy expertise, iDelsoft also supports teams looking to integrate AI-driven tools into their modernization workflows. They can design and implement custom AI solutions while ensuring robust security measures, such as SAST and secrets scanning, to prevent vulnerabilities introduced by AI-assisted code [23]. This combination of legacy system knowledge and responsible AI adoption makes iDelsoft a strong partner for modernizing even the most complex systems.

Conclusion

Refactoring legacy code isn't just a one-time task - it's a continuous process that keeps your systems resilient and maintainable. The strategies we've discussed, from creating safety nets with tests to making incremental changes, all work together to reduce risk and ensure stability. Whether you're writing characterization tests, using feature flags, or applying the Strangler Fig pattern for large-scale updates, each step is designed to make the process smoother and more effective.
The numbers back up the importance of this work. Addressing technical debt can allow engineers to shift up to 50% more of their time toward building new features rather than maintaining old ones [11]. In some cases, a single troublesome module can eat up as much as 30% of a development sprint just for bug fixes [10].
Martin Fowler explains it best:
"Refactoring is a controlled technique for improving the design of an existing code base. Its essence is applying a series of small behavior-preserving transformations... However, the cumulative effect... is quite significant." [10]
That cumulative effect is what matters most. By focusing on small, consistent improvements - supported by automated tests and guided by principles like the Boy Scout Rule - you can transform your codebase into something far easier to maintain and adapt over time.
If your team is grappling with substantial technical debt or navigating unfamiliar legacy systems, scaling these efforts can feel overwhelming. That's where iDelsoft comes in. They provide pre-vetted remote engineers experienced in modernizing legacy systems, whether you need help for a quick discovery sprint or a multi-month project. Their teams can be ready to assist in as little as 72 hours. By adopting these methods and leveraging expert support, your team can create a codebase that’s not just functional but future-ready.

FAQs
How do I decide whether to refactor or rewrite?

Deciding whether to refactor or rewrite a codebase is all about weighing key factors like code complexity, technical debt, and resource availability. While a full rewrite might seem like a clean slate, it comes with significant risks - such as losing critical insights embedded in the existing code. That's why incremental refactoring is often the safer choice. It allows for steady improvements while keeping the system operational.
To make the best decision, start by carefully evaluating the current codebase. Focus on areas that will deliver the most impact and consider strategies like the strangler pattern, which enables you to modernize parts of the system step by step without disrupting ongoing operations.

What tests should I write first if there are no tests?

If there are no existing tests, begin by creating characterization tests. These tests capture the current behavior of the code, serving as a baseline to ensure that changes don't unintentionally alter functionality. With these tests in place, you can confidently make small, incremental changes, verifying that all tests pass after each adjustment. This method helps minimize risks and keeps the system stable throughout the refactoring process.

How can I refactor safely without blocking new features?

To safely refactor code while still working on new features, focus on making small, step-by-step changes backed by thorough testing. Begin by writing tests to document the current behavior, so you can confirm that your updates don’t disrupt functionality. Consider using strategies like the strangler pattern, which allows you to gradually replace parts of the system without overhauling everything at once. Break down large refactoring tasks into smaller, manageable pieces that can be reversed if needed. Deploy these updates incrementally, and rely on continuous testing to ensure the system remains stable throughout the process.
Looking to scale more efficiently? Connect with iDelsoft.com! We specialize in developing software and AI products, while helping startups and U.S. businesses hire top remote technical talent—at 70% less than the cost of a full-time U.S. hire. Schedule a call to learn more!
2026-05-01 12:26 Engineering