Code Refactoring in the AI Era: What the Data Shows

There is a number that every engineering leader should know before making decisions about refactoring. Developers lose up to 42 percent of their time managing technical debt. That is not time spent on new features, on infrastructure improvements, or on security work. That is maintenance overhead that compounds quietly across every sprint until it becomes the primary reason delivery has slowed down and the team cannot explain why.

The irony in 2026 is that the tools meant to solve this problem are making it worse. GitClear and GitKraken analyzed 623 million real-world code changes from 2023 to 2026 and found that legacy refactoring, changes that remove or update code last touched more than twelve months ago, has fallen 74 percent since 2023. Duplicated code blocks in AI-assisted codebases rose eightfold in 2024. AI now generates 41 percent of new code, but without structured refactoring discipline, that speed produces accumulating clutter rather than sustainable architecture.

The refactoring problem in 2026 is not that teams do not know the techniques. It is that the context around those techniques has changed fundamentally: AI is introducing a new class of structural and security debt that most existing refactoring practices were not designed to address, and the cost of not refactoring is now quantifiable in ways it was not three years ago.

This guide covers the core refactoring techniques that still apply, where each technique fits in 2026's AI-assisted development context, where AI helps and where it introduces risk, and the enterprise-specific considerations that most guides skip. The goal is a framework your team can use to make better decisions about what to refactor, what to defer, and what to escalate to a structured software audit.

If your organization is carrying refactoring debt that has moved past what a sprint can address, Marka's engineers work with enterprises across healthcare, manufacturing, finance, and public administration on structured code modernization and technical debt remediation.

What Refactoring Actually Is and What It Is Not

Refactoring has a precise definition that gets blurred in practice, and the blurring causes expensive mistakes. Refactoring restructures existing code without changing what it does externally. A passing test suite before the change remains passing after it. The API contract stays. The function signature stays. What changes is the internal structure: shorter methods, clearer names, reduced duplication, a design that supports the next six months of feature work without accumulating risk.

Three things are consistently mislabeled as refactoring. Fixing a bug is debugging. Making code faster is optimization. Replacing a system or rewriting significant portions of it is a rewrite. Refactoring is the discipline in the middle, the one that keeps codebases maintainable between major rewrites and prevents maintenance overhead from compounding to the point where a rewrite becomes unavoidable.

The cost of skipping that middle ground is now measurable at portfolio scale. Software Improvement Group's State of Software 2026 report puts the direct labor cost of poor maintainability at roughly 870,000 euros per system per year for a low-maintainability system, using an average loaded developer cost of 150,000 euros per year. Across a ten-system portfolio held at that level, the figure approaches 9 million euros per year. These numbers cover engineering time only. They do not include the token cost of AI-assisted maintenance, which is becoming a second variable cost line for organizations that have adopted AI coding tools at scale.

The Refactoring Context That Has Changed in 2026

Understanding the techniques without understanding the context change produces refactoring guidance that is technically accurate and practically outdated. Three things are different in 2026 that change how refactoring should be approached.

AI-generated code has a different debt profile than human-written code. Senior engineers in 2026 report spending 20 to 35 percent more time on code review when junior developers lean heavily on AI assistants, a hidden cost that does not appear in velocity metrics but shows up in cycle time and in the post-release defect rate. AI-assisted code carries 1.7 times more issues than human-written code on average, and the specific issues, missing error handling, missing input validation, hallucinated dependencies, duplicated logic across files, are different from the issues that traditional refactoring practices were designed to catch. A refactoring approach built entirely around Martin Fowler's 2018 catalog is missing the failure modes that AI has introduced.

Refactoring activity has dropped to historic lows precisely when it is most needed. The same AI tools accelerating development have reduced refactoring activity because developers who can generate new code in seconds have less incentive to clean up old code carefully. The result is a quality regression that shows up in metrics: functional connectivity, a measure of how many times calls to other functions are programmed into new commits, has fallen 35 percent since 2023. Code is being written without being connected to the systems around it because the AI generates isolated solutions to isolated prompts.

The compliance cost of deferred refactoring has risen. For organizations subject to NIS2, DORA, or ISO 27001, code that cannot be audited because it has no tests, no documentation, and no legible structure is not just a quality problem. It is a compliance evidence problem. Regulatory frameworks require demonstrable control over software development practices, and a codebase that refactoring has been deferred on indefinitely cannot produce the evidence that audit functions require. The technical debt and the compliance debt are the same debt.

Extract Method and Rename: The Highest-Return Starting Point

These two techniques belong together as the starting point for almost every refactoring engagement. They are the fastest, safest, and most consistently valuable changes in any codebase, and modern IDEs automate the mechanical parts.

Extract Method takes a function doing three jobs and gives each job its own named function. The test for whether extraction is needed is whether you can describe what a function does without using the word "and." A function that calculates a subtotal, applies tax, applies a discount, and sends a notification email is four jobs in one place. Each job extracted into its own function produces code where the parent function reads like an outline and each child function reads like a sentence.

The practical benefit is not just readability. Functions with a single responsibility are testable in isolation, debuggable in isolation, and replaceable in isolation. The subtotal calculation can be updated, tested, and deployed without touching the notification logic. In large codebases with no test coverage, Extract Method applied systematically to the highest-traffic code paths is often the first step that makes subsequent, safer changes possible.

Rename is underestimated because it appears trivial. Variable names like data, temp, x, result, and flag exist throughout every codebase that has grown faster than its naming conventions. Each one is a moment of pause for every engineer who reads that code after the original author. Renamed to customerSubtotal, pendingOrderCount, isEligibleForDiscount, the same code reads without friction. The rename itself is automated by any modern IDE. The value compounds across every future reader of that code.

Where AI genuinely helps here: both Extract Method and Rename at the file or module level are tasks that AI tools execute reliably. The AI can see the full function, propose an extraction with a sensible name, and generate the test for the extracted behavior. This is the category of refactoring work where AI assistance provides real productivity gains without meaningful safety risk, provided the test suite is green before and after.

Refactoring by Abstraction: When to Do It and When to Wait

Abstraction lifts shared logic into a single place: a base class, a shared hook, a utility module. Two components sharing 80 percent of the same logic, three services parsing the same payload each in their own way, four endpoints applying the same validation rules with minor variations. Each of these is a candidate for abstraction.

The payoff is clear. When the shared logic needs to change, it changes in one place and the change propagates. When a new component needs the same behavior, it inherits rather than duplicates. The maintenance surface shrinks to a single well-understood module rather than being distributed invisibly across the codebase.

The risk is equally clear. Abstract too early, before the shared pattern is fully understood, and the abstraction constrains future development rather than enabling it. The classic mistake is abstracting two concrete cases that turn out to share surface-level similarity but have fundamentally different business rules underneath. The abstraction that seemed to simplify things makes the third case impossible to add cleanly, and the team ends up adding special cases to the abstraction until it is more complex than the duplication it replaced.

The practical rule that consistently works is the rule of three: wait until three concrete duplicates exist before abstracting. Two might be coincidence. Three is a pattern worth naming. This rule prevents the over-engineering that produces abstractions in search of use cases.

This is also one of the techniques where AI assistance carries the most risk. AI models produce abstractions that look architecturally clean and quietly fail on the edge case that the model never saw because it was not in the prompt. Any abstraction produced by an AI tool requires review by an engineer who understands the business rules the abstracted code encodes, not just the structural pattern the AI identified.

Replace Conditional with Polymorphism: The High-Leverage Structural Change

The switch statement or if-else chain that grows a new branch every quarter is the most reliable indicator that polymorphism should replace conditionals. A shipping cost calculation with fifteen if-branches for fifteen product types. A payment handler with separate code paths for twelve payment methods. A notification system with conditional blocks for eight delivery channels. Each of these has the same structural problem: adding a new case requires modifying existing code, and modifying existing code carries risk proportional to the complexity of what is already there.

Replacing conditionals with polymorphism means each case owns its own implementation: a product type subclass with its own shipping cost method, a payment method class with its own processing logic, a notification channel with its own delivery implementation. New cases slot in through the type hierarchy without touching any existing code. The open-closed principle, that software should be open for extension but closed for modification, is what this technique makes concrete.

The impact on enterprise codebases is disproportionate to the apparent simplicity of the technique. Codebases where business rules are encoded as large conditionals become progressively harder to modify safely as the number of cases grows, because every modification to the conditional carries risk of affecting cases that were not intended to change. Replacing the conditional with a polymorphic hierarchy makes each case independently modifiable and independently testable.

In .NET enterprise codebases, this technique appears most commonly in service classes and domain logic. The pattern of replacing large switch statements on entity types with strategy or visitor implementations is a standard refactoring that Marka's team applies as part of Cloud and Platform Modernization engagements where domain logic has accumulated in monolithic service classes over multiple years of development.

Red-Green-Refactor: The Safety Discipline That Makes Every Other Technique Safe

Red-Green-Refactor is not a technique in the same sense as the others. It is the discipline that makes all the other techniques safe to apply in production codebases with real users depending on them.

Write a failing test for the behavior you want to preserve. Confirm the test fails for the right reason. Make it pass with the minimum change needed. Then refactor the code while keeping the test green. The sequence is the safety net that separates refactoring from the high-risk activity of restructuring code without knowing whether the change broke something.

Most teams that report bad experiences with refactoring skipped this step. They restructured code that had no test coverage, shipped the change, discovered a regression two weeks later in production, and concluded that refactoring is dangerous. The correct conclusion is that refactoring without tests is dangerous. With tests, it is the safest kind of code change available, because the test tells you immediately whether the change broke anything.

For codebases with no existing test coverage, the starting point is characterization tests: tests that document what the code currently does, whether or not that behavior is what it should do. Characterization tests lock the current behavior in place before any structural changes begin. They do not validate correctness. They validate that refactoring has not changed behavior, which is the specific guarantee that refactoring requires.

The data on the value of this discipline is unambiguous. McKinsey's survey of top-quintile AI-enabled software teams found 31 to 45 percent gains in software quality, and the common thread across those top performers is disciplined testing around every change. The test suite is not an overhead on the refactoring work. It is what makes the refactoring work safe enough to do at the pace that delivers the quality gains.

Preparatory Refactoring: The Discipline That Prevents Entanglement

Preparatory refactoring applies Kent Beck's principle directly: make the change easy, then make the easy change. When a new feature requires touching a module that is tangled, the correct sequence is to clean the module first in a separate commit, then add the feature in the next commit. Two commits, two reviews, two independent rollback points.

The mistake teams consistently make is bundling the refactor and the feature into a single pull request. The combined change is harder to review, impossible to understand from the diff alone, and difficult to roll back if something breaks in production because the refactor and the feature are entangled in the same change set. When something breaks, and it will, the team cannot tell whether the refactor broke it or the feature broke it. The result is a multi-hour debugging session that ends with a revert of both changes and a commitment to ship them separately the next time.

Splitting the commits is the single process change that has the most leverage on enterprise refactoring outcomes. It requires a discipline of separating concerns at the commit level, not just at the code level, and it requires code review processes that treat a refactoring commit differently from a feature commit: the refactoring commit should change behavior in zero places, and the reviewer's job is to verify that guarantee.

For teams doing continuous refactoring as part of every sprint rather than as isolated cleanup projects, this discipline is what keeps the refactoring from creating more risk than it removes. It is also what makes the refactoring work reviewable, auditable, and explainable to the compliance functions that are increasingly asking about software development process controls.

Strangler Fig: The Only Safe Path Through Legacy Modernization

Some codebases sit past the point where in-place refactoring is the right tool. A twelve-year-old monolith with no test coverage, undocumented business logic embedded in stored procedures, and core functionality that cannot be taken offline for more than thirty seconds does not benefit from Extract Method applied one function at a time. It needs a different approach.

The Strangler Fig pattern wraps the legacy system with a new interface, redirects traffic function by function to modern replacement implementations, and allows the legacy code to be decommissioned progressively rather than in a single high-risk cutover. The name comes from the strangler fig tree, which grows around a host tree and gradually replaces it. The host tree continues to function while the strangler grows, and by the time the host is gone the replacement is already load-bearing.

The practical value for enterprise teams is that it separates the technical risk from the business risk. The business continues to operate on the legacy system, with zero downtime risk, while the modernization program delivers incremental replacements. Each replacement is independently testable, independently deployable, and independently revertable. The migration never depends on a single high-stakes cutover that would put production traffic at risk.

This is the pattern that Marka's Enterprise Platforms and Modernization practice applies to legacy .NET and Azure stack modernization engagements. It is also the pattern that the vibe coding cleanup work described in the previous section uses when the audit determines that a module's architecture is fundamentally incompatible with production requirements: build the replacement alongside the existing module, shift traffic progressively, decommission the old code once the replacement has proven itself under real load.

AI-Assisted Refactoring: Where It Helps and Where It Creates Risk

AI tools have changed the economics of refactoring. They have also changed the failure mode. Both need to be understood before building AI into a refactoring workflow.

Where AI provides genuine productivity gains without meaningful safety risk: mechanical renames across a file or module, Extract Method suggestions on functions the model can read completely, characterization test scaffolding for legacy code with no existing coverage, deprecated API replacements at the call-site level, and documentation generation for code that has no inline comments. These are tasks where the AI has the complete context needed to do the work correctly and the output can be verified by running the test suite.

Where AI creates risk that requires explicit mitigation: cross-file abstractions that require understanding business rules the model has never seen, seam identification for Strangler Fig migrations where the seam choices are architectural decisions not visible from the code alone, any change to code that lacks a passing test suite, and anything involving authentication, authorization, data storage, or external integrations where AI-generated code has a documented pattern of missing security controls.

The workflow that produces the best outcomes from AI-assisted refactoring is specific. Measure before touching anything: use a code health tool to identify which modules have the highest bug correlation and change failure rate, because refactoring high-entropy modules in active development paths produces the highest ROI. Scope the refactoring task to one unit of behavior: the red-green-refactor loop applied at the function or module level, with the AI given a clear instruction to preserve the behavioral contract the test encodes. Run mandatory human review on every AI-generated refactoring diff, with particular attention to cross-file effects that the model may have missed. Run the full test suite, not just the tests covering the changed function.

The most common mistake in AI-assisted refactoring is treating it as a strategy rather than an accelerator for one. The AI accelerates the mechanical execution of a refactoring decision that a human engineer has already made. Using AI to make the refactoring decision, which modules to refactor, which technique to apply, where the architectural seams are, produces systematically worse outcomes than using AI to execute a decision a human engineer has already reached.

Measuring Refactoring ROI: The Metrics That Matter

Enterprise organizations that invest in refactoring need to measure the return, not to justify the work but to prioritize it. Not all refactoring produces equal value, and the organizations that get the most from refactoring investment are the ones that target it at the modules where the technical debt cost is highest.

Change failure rate is the most direct measure of where refactoring would deliver the most value. The modules with the highest change failure rate, the ones most likely to produce incidents when changed, are the modules where structural problems are actively costing the organization in incident response and production risk. Refactoring those modules reduces the change failure rate, which reduces the operational cost of maintaining them.

Cycle time for features touching specific modules identifies where technical debt is slowing delivery. A feature that takes two days to implement in one part of the codebase and two weeks to implement in a structurally similar area of the codebase is telling you where the debt is creating the most friction. CodeScene ACE and similar code health tools quantify this as hotspot analysis: which files have the highest combination of change frequency and complexity, and therefore carry the highest ongoing maintenance cost.

Test coverage delta tracks whether refactoring is making the codebase safer or just reorganizing it. Refactoring that leaves test coverage unchanged has not improved the safety of future changes. Refactoring that increases coverage while improving structure is compound improvement: better organization and better safety at the same time.

Developer-reported friction is underused as a metric and highly predictive of where refactoring would deliver the most value. The modules that developers consistently describe as painful to work in, slow to understand, and risky to change are the modules where structural debt is creating the most day-to-day cost. A structured survey of development team friction, done quarterly and tracked over time, identifies refactoring priorities with more practical accuracy than static analysis metrics alone.

When Refactoring Is the Wrong Call

Refactoring is a tool with a specific application. Three situations consistently call for something other than refactoring.

The target platform is being retired. Refactoring code inside a framework or runtime approaching end-of-life postpones the real work. Every hour spent improving code on a platform that will be decommissioned is an hour not spent on the migration. The exception is refactoring that creates seams for a Strangler Fig migration: cleaning up the legacy system's external interface to make the replacement easier to build alongside it.

The code has no test coverage and is live in production. Refactoring blind is the fastest way to ship a regression to paying customers. The right sequence is characterization tests first, then refactoring. If the codebase resists characterization testing because its dependencies cannot be isolated, the Strangler Fig pattern is the safer path than in-place refactoring.

The cost of understanding the code exceeds the cost of replacing it. This is the honest call on some inherited codebases, and it is a call that requires data rather than instinct. If no current team member can explain what a critical module does within a reasonable time investment, and the module is small enough to be rewritten cleanly, the rewrite may be cheaper than the archaeology. This decision benefits from a structured external assessment that can evaluate the codebase without the organizational context that can make the archaeology feel more necessary than it is. Marka's team conducts exactly this kind of assessment as an input to modernization engagements, specifically to determine which components benefit from refactoring and which have passed the point where refactoring is the right investment.

What to Do Next

Three actions are worth taking before the next planning cycle where refactoring competes for sprint capacity.

Run a hotspot analysis on your codebase. Tools like CodeScene, SonarQube, and NDepend can identify which files have the highest combination of change frequency and complexity. Those hotspots are where refactoring delivers the most return, because they are the modules the team touches most often and where structural problems create the most repeated friction.

Separate your refactoring commits from your feature commits. This single process change reduces risk, improves reviewability, and produces cleaner audit trails for compliance functions. It does not require a new tool or a new process. It requires a team agreement that refactoring and feature work do not ship in the same commit.

Assess your AI-assisted refactoring workflow against the risk framework above. If your team is using AI tools for refactoring without mandatory human review and a green test suite on every change, the risk profile of that workflow is higher than it appears from the acceptance rate. Building the review discipline into the workflow before expanding AI-assisted refactoring is the investment that determines whether the productivity gains are sustainable.

For organizations where the refactoring debt has compounded to the point where sprint-level cleanup is insufficient, Marka's engineering team delivers structured code modernization and refactoring programs across the industries where code quality and compliance requirements are most demanding. You can reach the team.