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AI can help developers refactor code, but it does not guarantee faster delivery, better maintainability, or safer software. The evidence available in 2026 is mixed: one controlled study found a shorter median completion time for a particular task, while a separate analysis of selected readability-related agent commits found that conventional maintainability and complexity measures often worsened. The practical question is not whether an AI tool can change code; it is whether your team can validate the change, preserve behavior, and review it at the pace it is produced.
What AI-powered code refactoring means
Refactoring changes a program’s internal structure with the aim of improving code quality without changing its externally observable behavior. That definition, used by the authors of Agentic Refactoring: An Empirical Study of AI Coding Agents, sets an important boundary: a change that alters behavior may still be useful, but it is not a behavior-preserving refactor. An AI proposal must be checked against that boundary rather than assumed to meet it.
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AI-powered refactoring covers several workflows. A developer might ask a chat assistant to explain a function and suggest a rewrite, accept inline code completions while editing, or delegate a multi-step change to an agent that inspects files and modifies code. These workflows differ in autonomy and review burden; none makes tests, code review, or security analysis optional.
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The statistics below come from different populations and methods. Survey responses describe what respondents said; controlled studies measure particular tasks under particular conditions; observational commit analyses describe changes found in a selected sample. They should not be combined into a single estimate of AI’s effect on software development.
#1 Best Overall
| Finding | What was measured | How to interpret it |
|---|---|---|
| 54% average share of code respondents said was AI-generated in 2026, compared with 28% in 2025 | State of AI 2026 open survey: 7,258 developers responded overall, and 6,420 answered the code-share question. | This is respondents’ self-reported share, not a representative estimate of all code written. The survey publisher warns that an AI-focused open survey may have selection bias. |
| 30.7% shorter median completion time | Authors of a 2026 Empirical Software Engineering study reported this result for AI-assisted participants on Task 1. | The result applies to one study task. It is not a universal productivity multiplier or evidence of faster organization-wide delivery. |
| No frequentist evidence that AI use affected average CodeHealth after later manual evolution | The same study examined code after participants continued to evolve it manually. | The authors noted uncertainty related to sample size and task interpretation. Their Bayesian analysis estimated a positive CodeHealth effect for habitual AI users, while Java proficiency had a stronger influence on later outcomes than AI use. |
| 56.1% had a lower Maintainability Index; Cyclomatic Complexity rose in 42.7% | Authors of the MSR 2026 readability study analyzed 403 commits selected for readability-related keywords. | This is an observational, selected sample—not a general failure rate for AI refactoring. The changes had readability-related intent; that intent did not ensure improved conventional metrics. |
| 42.4% targeted logic complexity; 24.2% targeted documentation | The same 403-commit analysis classified the focus of the changes. | In that sample, agents addressed logic and documentation more often than surface concerns such as naming or formatting. |
| Roughly twice as many security-risk violations in AI-generated code as in human-written code | Software Improvement Group (SIG) reported this comparison from its own 2026 testing. | This is SIG’s finding, not a universal rate across tools, languages, or organizations. |
| 85% said AI shifted the bottleneck from writing code to reviewing and validating it; 82% were concerned about technical debt; 43% could not reliably distinguish AI-generated from human-written code in their codebase | GitLab and The Harris Poll’s 2026 survey covered 1,528 developers and technology buyers across six countries. | These are respondents’ views, not audited measurements of every organization’s workflow. |
| 90% of technology professionals use AI at work | Share reported on SIG’s 2026 State of Software publication page. | This is a SIG-reported figure for the population SIG describes; it is not interchangeable with the State of AI open survey’s respondents. |
Taken together, these findings support a conditional conclusion: AI can speed up a bounded coding task, but task speed alone says little about behavior preservation, maintainability, security, or the time spent reviewing and integrating the result.
Why faster code generation may not mean faster delivery
Generation is only one stage of a software change. A team must understand the proposal, inspect the diff, run relevant tests, investigate failures, check security implications, and integrate the result. If changes arrive faster than reviewers can evaluate them, the work may move from implementation queues into review and validation queues instead of reaching production sooner.
In GitLab and The Harris Poll’s 2026 survey, 85% of respondents agreed that AI had shifted the bottleneck from writing code to review and validation. The same survey found that 43% could not reliably distinguish AI-generated from human-written code in their codebase. Those responses point to two different operational needs: sufficient review capacity and useful provenance. Tracking assistance can help a team understand a change’s origin and accountable owner, but it does not establish that the code is correct.
The Tool Desk
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Risks to manage when using AI for refactoring
Behavior changes disguised as structural cleanup
A rewrite can look simpler while changing edge-case behavior, error handling, data transformations, or interfaces. Define the intended behavior boundary before accepting the change. Compare the diff with the requested scope and run tests that exercise the affected behavior. Tests provide evidence, not proof of every functional or non-functional property, so keep independent review and security checks in the process.
Readability intent mistaken for quality improvement
The MSR 2026 analysis found lower Maintainability Index in 56.1% of its 403 selected readability-related commits and increased Cyclomatic Complexity in 42.7%. These results do not show that AI refactoring generally makes code worse; they do show why labels such as “cleanup,” shorter code, fluent comments, or a clearer explanation are not substitutes for inspecting the resulting structure and relevant quality measures.
Review, ownership, and traceability debt
When a team cannot tell what was changed, why it was changed, or who owns the result, later maintenance becomes harder. GitLab and The Harris Poll’s findings on code provenance and technical-debt concerns are survey responses, but they give teams concrete questions to ask: can reviewers identify the purpose and scope of an AI-assisted change, and is a named developer accountable for it?
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Security risk hidden by plausible output
SIG’s 2026 testing found roughly twice the security-risk violations in AI-generated code compared with human-written code. Because the result comes from SIG’s testing, it should not be applied as a universal rate; it is still a reason not to treat plausible-looking output as security-reviewed output. Include the checks appropriate to the language, application, and threat model in the existing development process.
How to choose an AI refactoring tool or workflow
The 2026 evidence here does not establish a current vendor ranking, product feature comparison, or pricing recommendation. Instead, compare tools against the work your team actually needs to do and its ability to verify the result.
Rank #4
| Decision area | Questions to ask |
|---|---|
| Task and autonomy | Is the tool offering inline completions, conversational suggestions, or an agent that plans and executes a multi-step change? What level of autonomy fits the task and review capacity? |
| Repository context | Can the workflow account for relevant files, tests, project conventions, and architecture? How will the developer identify assumptions or missing context? |
| Diff inspection and validation | Can a developer inspect the complete change, run tests, and reject or revise parts of it? Does generation remain separate from independent review? |
| Traceability and governance | Can the organization record the intended purpose, what was AI-assisted, and the accountable owner? What policies apply to repository access and generated changes? |
| Security and maintainability | Which additional checks fit the team’s process, and how are their results reviewed? Do not treat a vendor claim as proof that generated output is safe or maintainable. |
| Cost, limits, and support | Before adopting a product, verify its current official pricing, usage caps, supported models, language support, and enterprise terms. These details are not established by the studies and reports summarized here. |
Tool category is not a quality rating. A more autonomous agent may reduce some manual steps but can also make a wider change that demands careful inspection. A completion assistant may keep the developer closer to each edit but still produce code that requires the same behavioral and security validation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical validation process for AI-assisted refactoring
Use a process that treats AI output as a proposal and makes the intended behavior boundary explicit.
- State the goal and boundary. Describe the structural improvement sought and identify behavior, interfaces, or constraints that must remain unchanged.
- Limit the scope. Ask for a focused change that can be reviewed as a coherent diff. Avoid accepting unrelated rewrites simply because they appear alongside the requested refactor.
- Inspect the diff. Check the affected code and surrounding context for behavior changes, unnecessary complexity, altered error handling, or unsupported assumptions.
- Run relevant tests and checks. Use the project’s tests and applicable static, quality, and security checks. Investigate failures rather than treating a successful generation step as validation.
- Review independently and record ownership. Have a developer assess whether the change meets the goal, document its purpose and accountable owner, and retain enough context for future maintenance.
- Evaluate outcomes over time. Compare review effort, rework, defects, and relevant quality measures—not just code-generation time—before expanding the workflow.
These safeguards align with eu-LISA’s 9 July 2026 report, which recommends monitoring technological developments, regularly evaluating AI tools, and ensuring sufficient resources to review AI-generated code. That is public-sector guidance, not a universal regulation or a guarantee that one procedure makes a tool safe.
Best Value
How to interpret organizational and code-quality claims
SIG’s 2026 State of Software report draws on benchmarks across tens of thousands of systems. It reports that 86% of code fell below its recommended maintainability rating, that 71% had a low degree of security controls, and estimates €870,000 in annual developer-time savings per system from reducing code-level technical debt. These are SIG’s benchmark and report figures, not results of the controlled refactoring study; the savings estimate should not be read as a guaranteed return from adopting an AI assistant.
Likewise, adoption figures from one survey should not be used to predict another organization’s usage, and a task-time result should not be presented as an organization-wide productivity gain. The more useful decision measure is whether AI-assisted changes deliver the intended behavior and quality while fitting the team’s review, testing, and security capacity.
Bottom line
AI-powered refactoring is best treated as a way to propose or accelerate code changes—not as an autonomous quality-control system. The 2026 evidence shows both task-level speed gains and meaningful uncertainty about maintainability, security, and downstream delivery. Adopt a workflow only when the team can define what must not change, validate the result, review it independently, and track whether the complete delivery process improves.
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