Every engineering team has that backlog. The one with refactoring tickets from six months ago. A year ago. Sometimes longer.
"Migrate to new API pattern." "Standardize error handling." "Update class components to hooks." "Remove deprecated library usage."
These tickets sit there, aging, because they're important but never urgent. Features have deadlines. Refactoring doesn't. So the backlog grows.
Now teams are asking: can AI automate refactoring work that's been on the backlog for months? Can it finally clear the technical debt that humans keep deferring?
The short answer: yes, for many types of refactoring. But not all.
Here's what AI can handle, what it can't, and how to approach your backlog strategically.
Why Refactoring Gets Deferred
Before diving into AI capabilities, it's worth understanding why refactoring accumulates in the first place.
It's Never Urgent
Refactoring improves code without changing behavior. The system works today whether you refactor or not. Every sprint, features win the prioritization battle because they deliver visible value.
The Scope Is Intimidating
"Migrate to new pattern" sounds simple until you realize it touches 200 files. The effort required makes the ticket feel impossible to fit into a normal sprint.
The Risk Feels High
Refactoring that touches many files could break things. Without confidence in test coverage, teams hesitate to make sweeping changes.
It's Tedious Work
Even when refactoring is straightforward, it's boring. The same mechanical transformation applied hundreds of times. Nobody wants to spend their week on it.
These factors combine to create backlogs that grow indefinitely. The refactoring that was "nice to have" six months ago is now blocking modernization efforts—but it's still not getting done.
What AI Can Automate
AI excels at refactoring that follows patterns. If you can describe the transformation rule, AI can probably apply it at scale.
Pattern-Based Transformations
The best candidates for AI automation are refactorings where:
- The before and after states are clearly defined
- The transformation is mechanical (same rule everywhere)
- Success can be verified by tests or static analysis
Examples:
Syntax modernization:
@devonair convert all var declarations to const/let
@devonair migrate to optional chaining where applicable
@devonair update to nullish coalescing operators
API migrations:
@devonair migrate from moment.js to dayjs
@devonair update all fetch calls to use the new API wrapper
@devonair replace legacy Logger with new logging service
Component updates:
@devonair convert class components to functional components with hooks
@devonair migrate from HOCs to custom hooks
@devonair update to new prop naming conventions
Code organization:
@devonair reorganize imports to match style guide
@devonair extract inline styles to styled-components
@devonair move types to dedicated .types.ts files
Large-Scale Consistency
AI handles scale that's impractical for humans. Updating 500 files with the same transformation takes AI minutes and takes humans days (with inevitable inconsistencies).
@devonair standardize error handling across all API calls
AI applies the exact same pattern everywhere. No variation from developer fatigue or different interpretations.
Tedious But Straightforward
AI excels at work that's boring but not complex:
- Renaming variables across a codebase
- Updating import paths after restructuring
- Adding type annotations to untyped code
- Removing deprecated API usage
- Cleaning up unused imports
This is exactly the work that sits in backlogs because humans don't want to do it.
What AI Can't Automate
AI has limits. Some refactoring requires human judgment that AI can't reliably provide.
Architectural Decisions
AI can apply patterns but can't decide which patterns to use. Questions like:
- Should this be a microservice or stay in the monolith?
- Is this the right abstraction boundary?
- Should we use inheritance or composition here?
These require understanding business context, team capabilities, and future direction that AI doesn't have.
Ambiguous Transformations
When the "right" answer isn't clear, AI struggles:
- Refactoring where multiple valid approaches exist
- Situations where business rules affect the choice
- Cases where the existing code's intent is unclear
AI needs clear rules. Ambiguity requires human judgment.
Novel Patterns
AI works from learned patterns. If your refactoring requires inventing a new approach rather than applying an existing one, AI can't lead that work.
Semantic Changes Disguised as Refactoring
Some "refactoring" actually changes behavior:
- Performance optimizations that alter timing characteristics
- Error handling changes that affect user experience
- Caching additions that change data freshness
AI assumes refactoring preserves behavior. Changes that intentionally alter behavior need human design.
Assessing Your Backlog
Look at your refactoring backlog and categorize each item:
Category A: Fully Automatable
Pattern-based transformations with clear rules:
- "Update all X to Y pattern"
- "Migrate from library A to library B"
- "Apply new coding standard across codebase"
AI approach: Point AI at the codebase, describe the transformation, review the PR.
Category B: AI-Assisted
Refactoring that needs human decisions but has automatable parts:
- "Improve error handling" (human decides approach, AI applies it)
- "Refactor for testability" (human identifies changes, AI executes)
AI approach: Human makes design decisions, AI handles the mechanical transformation.
Category C: Human Required
Architectural or judgment-heavy refactoring:
- "Redesign module boundaries"
- "Improve performance" (without specific approach)
- "Make code more maintainable" (vague)
AI approach: AI can assist with analysis but humans must lead.
A Strategy for Clearing the Backlog
Here's how to approach a long-neglected refactoring backlog with AI assistance.
Step 1: Inventory and Categorize
Go through every refactoring ticket. For each one:
- Is the transformation clearly defined?
- How many files are affected?
- Is there test coverage?
- Which category (A, B, or C)?
Step 2: Quick Wins First
Start with Category A items that have good test coverage. These are low-risk and demonstrate value quickly.
@devonair migrate all moment.js usage to dayjs
Result:
- Updated 47 files
- All tests pass
- PR ready for review
A backlog item that's been sitting for a year gets cleared in an afternoon.
Step 3: Build Confidence
As AI-generated PRs prove reliable, expand scope:
- Tackle larger transformations
- Address items with less test coverage (add tests first)
- Move to Category B items with AI handling execution
Step 4: Human-AI Collaboration for Complex Items
For Category B and C items:
- Human analyzes and decides on approach
- Human documents the pattern/rules
- AI applies the transformation
- Human reviews and refines
The human provides judgment; AI provides scale.
Step 5: Prevent Future Accumulation
Once the backlog is clear, keep it clear:
- Automate pattern enforcement (lint rules, AI checks)
- Do refactoring continuously rather than batching
- Address new technical debt before it ages
Real Backlog Examples
Example 1: "Migrate to TypeScript strict mode"
Backlog age: 8 months
Category: A (automatable)
Scope: 300+ files with any types
AI approach:
@devonair enable TypeScript strict mode
@devonair add proper types to replace any usage
AI analyzes usage patterns, infers types from context, and updates files. Developers review the PR, fix edge cases AI got wrong, merge.
Result: 8-month-old ticket cleared in 2 days.
Example 2: "Standardize API error handling"
Backlog age: 14 months Category: B (AI-assisted) Scope: 150 API calls across the app
AI approach:
- Human decides on error handling pattern
- Human documents the standard approach
- AI applies pattern to all API calls:
@devonair wrap all API calls with standardErrorHandler pattern
Result: Human spends 2 hours on design, AI spends 1 hour on transformation, 14-month-old ticket cleared.
Example 3: "Break up the god object"
Backlog age: 2 years Category: C (human required) Scope: 5,000 line class that does everything
AI approach:
- AI analyzes dependencies and usage patterns:
@devonair analyze UserManager class for decomposition opportunities
- Human decides on new structure
- AI assists with mechanical extraction:
@devonair extract authentication methods to AuthService
@devonair extract profile methods to ProfileService
@devonair update all usages to use new services
Result: Human leads architecture decisions, AI handles tedious extraction and updating. 2-year-old ticket finally addressed.
Setting Realistic Expectations
AI won't clear your entire backlog overnight. But it fundamentally changes what's feasible.
What Changes
Before AI:
- Large refactoring requires dedicated sprints
- Mechanical work competes with creative work for developer time
- Backlogs grow because capacity is limited
After AI:
- Large refactoring happens through PR review
- Mechanical work is automated, developers focus on judgment
- Backlogs shrink because execution capacity is abundant
What Doesn't Change
- Architectural decisions still need human judgment
- Test coverage still determines refactoring confidence
- Vague tickets still need clarification before execution
- Review and verification still take human time
Getting Started Today
If you have refactoring sitting in your backlog, try this:
- Pick one ticket that's clearly defined and pattern-based
- Describe the transformation to an AI tool
- Review the generated PR for correctness
- Merge or adjust based on what you see
One successful AI-assisted refactoring demonstrates more than any amount of theorizing. See what AI can do with your specific codebase and backlog.
That refactoring work that's been on the backlog for months? It might be done by end of week.
FAQ
How do I know if AI can handle a specific refactoring?
Ask: "Can I describe this as a clear before/after transformation?" If yes, AI can probably handle it. If the ticket requires judgment calls or the approach is undefined, humans need to lead.
What if AI makes mistakes during refactoring?
Every AI-generated change goes through your normal PR review and testing process. Mistakes get caught the same way human mistakes get caught. Start with well-tested areas to build confidence.
Will AI refactoring break my tests?
If refactoring is truly behavior-preserving, tests should pass. If tests fail, either the refactoring changed behavior (fix the refactoring) or the tests were testing implementation details (fix the tests).
How do I handle refactoring without good test coverage?
Add tests first. AI can help here too—generate tests for the current behavior, then refactor with confidence. Don't do large-scale refactoring without a safety net.
Can AI handle refactoring across multiple repositories?
Yes, though you may need to run it on each repo separately and coordinate. Some AI tools support multi-repo operations for organization-wide consistency.
Is this safe for production code?
As safe as any other code change. AI generates the PR, you review it, your CI tests it, you decide whether to merge. The normal safeguards apply.