Refactoring carries a positive reputation in software engineering.
It suggests that developers are improving structure without changing observable behavior. The code should become easier to understand, safer to modify, and less expensive to maintain.
That is the intention.
But the word “refactor” describes what a change claims to do. It does not prove what the change actually achieved.
An AI coding agent can rename variables, extract methods, add documentation, reorganize files, and simplify a local function. The result may look cleaner in the diff and still leave the wider codebase harder to maintain.
The real question is not whether a refactoring task was completed.
It is whether the change created durable improvement.
Refactoring activity is not refactoring success
A refactor can improve one dimension while weakening another.
For example:
- extracting a method may reduce the size of one function but increase coupling,
- adding abstractions may improve local consistency while making navigation harder,
- adding comments may improve explanation while leaving the underlying complexity untouched,
- splitting a file may distribute logic without reducing it,
- renaming may improve vocabulary while increasing the size of the change,
- and removing duplication in one place may introduce a more rigid shared dependency.
This creates an important distinction:
| Signal | What it actually tells you |
|---|---|
| A commit is labeled “refactor” | The stated intention of the change |
| Tests remain green | Known behavior still satisfies current checks |
| Lines or files were reorganized | The code’s shape changed |
| Complexity decreased | One structural proxy improved |
| Later rework declined | The change may have reduced future correction cost |
| The effort survived later changes | The improvement remained active as the codebase evolved |
The first three signals describe the change at merge time.
The last three begin to describe its outcome.
AI readability refactors produced unexpected results
A 2026 study titled Do AI Agents Really Improve Code Readability? examined readability-oriented changes created by AI coding agents.
The researchers first identified agent-generated commits whose messages explicitly referred to readability, clarity, understandability, or related concepts. They then evaluated 403 commits for which valid before-and-after Python code states could be reconstructed.
The agents most frequently targeted:
- complex, long, or inadequate logic: 42.4%,
- incomplete or inadequate documentation: 24.2%,
- bad identifiers: 14.3%,
- and inconsistent formatting: 14.3%.
Those targets sound reasonable.
The measured outcomes were less reassuring.
| Reported outcome | Share of evaluated commits |
|---|---|
| Maintainability Index decreased | 56.1% |
| Cyclomatic Complexity increased | 42.7% |
| Lines of code increased | 71.5% |
| Cyclomatic Complexity improved | 9.7% |
The researchers concluded that readability-oriented agent commits did not necessarily improve structural code quality and were often accompanied by greater code volume and lower maintainability according to the evaluated metrics.
That does not mean 56.1% of AI refactors were objectively bad.
Maintainability Index and Cyclomatic Complexity are proxies. They cannot fully capture clarity of intent, domain correctness, architectural fit, or the experience of the engineers who will maintain the system. The study also focused on Python projects and selected commits through readability-related keywords.
But the result establishes a useful warning:
A change can be created for readability, merged as a refactor, and still move traditional quality indicators in the wrong direction.
AI agents tend to favor local improvements
A separate large-scale study of agentic refactoring analyzed 15,451 refactoring instances across 12,256 pull requests and 14,988 commits.
It found that coding agents commonly performed localized, consistency-oriented operations such as:
- changing variable types,
- renaming parameters,
- renaming variables,
- and making other narrowly scoped structural edits.
The study also found small but statistically significant improvements in some structural metrics, particularly for medium-level changes.
This is important because the evidence is not one-sided.
AI agents can perform useful refactoring. They can remove repetitive work, apply consistent transformations, help developers explore alternatives, and accelerate carefully constrained cleanup.
The limitation is that local correctness is not the same as system-level improvement.
An agent may successfully improve the method it was asked to change while lacking the organizational history, architectural context, ownership knowledge, or future roadmap needed to judge whether the new structure belongs in the wider system.
A clean local diff can create a new global burden.
Quality and security can move in different directions
Another 2026 study examined quality and security signals in AI-generated Python refactoring pull requests.
The results were mixed:
| Observation | Reported result |
|---|---|
| Studied changes improving at least one evaluated quality attribute | 22.5% |
| Modified files introducing new Pylint findings | 24.17% |
| Modified files introducing new Bandit findings | 4.7% |
| Analyzed pull requests that were merged | 73.5% |
These figures should not be combined into a single success or failure score. A change can improve one attribute while introducing a convention issue elsewhere. A static-analysis finding does not automatically mean the refactor should be rejected. A merged pull request does not prove that its long-term outcome was positive.
That is exactly the problem.
Refactoring creates a multi-dimensional result, but engineering organizations often reduce it to a binary event:
- merged or rejected,
- tests passed or failed,
- refactor completed or not completed.
AI makes this simplification more dangerous because it can produce a larger volume of plausible transformations.
The diff shows the intervention, not the outcome
A code review can inspect what changed.
It can ask whether the new structure is understandable, whether behavior is preserved, whether abstractions are appropriate, and whether the tests are sufficient.
But review still happens before the code experiences future requirements.
The most revealing evidence appears afterward:
- Does the refactored code remain active?
- Is it extended or immediately rewritten?
- Do later developers reuse the new abstraction?
- Does the same area continue to attract corrective work?
- Does the refactor reduce rework or merely move it?
- Does the change survive once another team owns the module?
- Does AI-assisted cleanup create leverage at the repository level?
A refactor can look convincing on the day it is merged and fail its real test during the next six months of change.
Three possible outcomes of a refactor
Post-merge evidence helps distinguish at least three different outcomes:
| Outcome | Typical pattern |
|---|---|
| Durable improvement | The change remains active, supports later work, and reduces repeated correction |
| Neutral reshaping | The code changes form, but later maintenance behavior remains broadly similar |
| Deferred maintenance | The change is soon rewritten, repeatedly corrected, or replaced by another restructuring |
These categories are not automatic labels.
A short-lived refactor may be completely reasonable if requirements changed. A long-surviving change may remain untouched because the feature is inactive. A high-refactoring repository may be modernizing a legacy system rather than suffering from poor initial work.
The goal is not to classify every refactor mechanically.
The goal is to make the trajectory visible enough for engineering judgment.
GitMe measures beyond the refactor label
GitMe complements code review, tests, static analysis, and observability by adding post-merge codebase history.
It does not assume that every refactor is good or that every rewrite is waste.
Instead, several signals can be examined together.
AI Leverage
AI Leverage measures AI’s multiplier effect on engineering output rather than merely counting usage.
It can be viewed at developer, project, and repository levels.
A high AI Leverage value can show where AI is associated with stronger output. It should then be interpreted alongside what happens to that output later.
Refactoring and rework
Refactoring indicates structural change. Rework reveals where recent effort is being revised, replaced, or corrected.
Neither should be interpreted alone.
Healthy teams refactor intentionally. Fast-moving products also revisit recent work. The useful signal is the pattern: where refactoring reduces future friction and where it begins another maintenance cycle.
Effort Survival Cohort
Effort Survival Cohort shows how much earlier modeled engineering effort remains active as the codebase changes.
It is not employee retention and it is not a universal quality score.
For refactoring, it adds a durability question:
Did the redesigned code continue to carry value, or did later work replace it?
Evaluate refactoring at more than one level
A single developer-level number can be misleading.
GitMe allows AI Leverage and related engineering signals to be examined across developers, projects, and repositories.
Each level answers a different question:
| Level | Useful question |
|---|---|
| Developer | Which practices are associated with durable AI-assisted improvements? |
| Project | Where does refactoring reduce or increase repeated work? |
| Repository | Is the codebase becoming easier to evolve as AI-assisted change grows? |
This makes it possible to separate an individual change from its environment.
A developer may create durable refactors in one repository and struggle in another with unclear boundaries. A project may show heavy rework because requirements are unstable rather than because engineering quality is low. A repository may need architectural investment even when individual contributors are performing well.
Context prevents measurement from turning into simplistic ranking.
A better refactoring review has three time horizons
Engineering leaders can evaluate AI-assisted refactoring across three stages:
| Time horizon | Evidence |
|---|---|
| Before merge | Design intent, tests, static analysis, review quality, architectural fit |
| After release | Reliability, incidents, rollback behavior, operational impact |
| Over continued change | Rework, additional refactoring, reuse, ownership, and effort survival |
The first stage asks whether the refactor is safe to accept.
The second asks whether it behaves correctly in production.
The third asks whether it improved the future of the codebase.
All three matter.
What engineering leaders should ask
A useful refactoring measurement system should support questions such as:
- Which AI-assisted refactors remain active after later feature work?
- Where does refactoring reduce rework?
- Which repositories repeatedly refactor the same areas?
- Are agents making local improvements while system complexity continues to rise?
- Which developers use AI to simplify code without creating repeated correction?
- Where would stronger architectural context improve agent output?
- Are teams rewarded for refactoring volume or for durable improvement?
- Does higher AI Leverage coincide with code that others can extend successfully?
These questions should lead to coaching, process improvement, better context, and stronger engineering constraints.
They should not become a leaderboard that discourages necessary refactoring.
Refactoring needs outcome evidence
The label “refactor” is useful. It tells reviewers what the change intends to accomplish.
It is not the final verdict.
AI can make refactoring faster, more consistent, and easier to initiate. It can also make organizations produce more transformations than they can meaningfully evaluate.
The answer is not to avoid AI-assisted refactoring.
The answer is to measure it across time.
A refactor should not be judged only by how clean the diff looks, whether the tests pass, or whether the pull request merges.
It should also be judged by what happens to the code afterward.
Related GitMe Reading
- Green Tests Can Still Hide Decaying Software
- The Hidden Maintenance Tax of AI-Assisted Coding
- AI Didn’t Eliminate Engineering Work. It Made It Invisible.
- AI-Generated Code Should Be Measured by How Long It Survives in Production
Sources
- Horikawa et al. — Do AI Agents Really Improve Code Readability?
- Horikawa et al. — Agentic Refactoring: An Empirical Study of AI Coding Agents
- Almukhtar et al. — Quality and Security Signals in AI-Generated Python Refactoring Pull Requests
- Orlanski et al. — SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks
- Google Research — What Improves Developer Productivity at Google? Code Quality.
- GitClear — The Maintainability Gap: AI Code Quality in 2026
Measure Refactoring by What Survives
Connect AI Leverage with refactoring, rework, and Effort Survival Cohort signals to see which changes create durable improvement instead of another maintenance cycle.
Start with GitMe