AI RNG: Practical Systems That Ship
Refactoring is where good engineers get accused of breaking things they did not touch. The code compiles, tests pass, and yet something subtle shifts, a runtime behavior changes, or a performance regression appears in a corner nobody anticipated. The larger the codebase, the more refactoring feels like moving furniture in a dark room.
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A refactoring plan is how you turn that darkness into a sequence of safe, reviewable steps. AI can accelerate the mechanical work, but the plan is still the thing that protects users and preserves trust.
Why “big refactors” fail
Most refactors fail for the same reasons:
- Too many changes land at once, making review and rollback difficult.
- There is no stable definition of correct behavior.
- The team cannot reproduce production-like conditions in a test environment.
- The refactor rearranges code while also changing semantics.
- The rollout does not include a stop signal.
A plan fixes these by separating concerns: behavior protection first, mechanical change second, semantic improvement last.
Start by naming the seams you want to create
Spaghetti code is not only messy. It is coupled. The first goal is to identify the seams where you want boundaries to exist.
Typical seams include:
- Input parsing separated from business rules
- Business rules separated from side effects
- IO wrapped behind interfaces
- Serialization isolated to boundary modules
- Domain types separated from transport DTOs
A seam is valuable if it reduces the surface area that must be understood at once.
Make a behavior safety net before rearranging code
Before you move code, protect behavior. You can do that in several ways:
- Add unit tests around pure logic.
- Add integration tests at module boundaries.
- Add characterization tests for legacy behavior at key entry points.
- Add logs and metrics for critical paths so you can detect drift after deployment.
AI is useful here for generating test scaffolding, but the contract must be explicit: what should remain true after the refactor.
A helpful safety-net table:
| Area | Protection type | Pass signal |
|---|---|---|
| Critical user flows | integration tests | deterministic pass in CI |
| Legacy corner behavior | characterization tests | output matches before changes |
| Performance hotspots | benchmarks | regressions detected early |
| Error boundaries | contract tests | correct failures and messages |
Decompose the refactor into mechanical steps
A reviewable refactor is a sequence of commits where each commit has a single purpose. This is where AI can save real time, because it can propose the ordering and generate repetitive edits.
A strong commit sequence often looks like:
- Introduce types and interfaces without changing behavior.
- Add adapters that allow old and new code paths to coexist.
- Move code behind new boundaries with thin wrappers.
- Delete dead paths only after the new path proves stable.
- Normalize naming and folder structure at the end.
The principle is simple: keep the system runnable at every step.
Use dual-path techniques to reduce fear
When the stakes are high, you can run old and new implementations in parallel:
- Shadow mode: compute both results, return the old one, compare and log differences.
- Sampling: route a small fraction of traffic to the new path.
- Feature flags: allow instant rollback without redeploy.
These approaches turn refactoring from a leap into a walk.
A useful comparison table for choosing technique:
| Technique | Best for | Cost | Risk |
|---|---|---|---|
| Shadow mode | pure computations | medium | low |
| Sampling | API handlers | medium | medium |
| Feature flags | wide behavior changes | low to medium | depends on discipline |
Let AI produce “mechanical commits” while you own semantics
AI is strong at mechanical edits:
- Renaming symbols consistently
- Extracting functions with stable signatures
- Moving files and updating imports
- Converting repetitive patterns into helpers
- Adding wrappers and interfaces
AI is weaker at hidden semantics: concurrency, ordering, caching, and error behavior. When you use AI for refactoring, constrain it:
- Require the plan to specify what remains behavior-identical.
- Require each step to be verifiable by tests.
- Require a rollback mechanism for each risky step.
A plan that cannot be verified is not a plan, it is a wish.
Build a module map that reviewers can understand
Refactors lose support when nobody can see the destination. Provide a simple module map early:
- What modules exist after the refactor.
- What responsibilities live where.
- What dependencies are allowed.
- What boundaries are enforced.
A reviewer should be able to understand the shape without reading every diff.
Verify with production-like checks
Even strong tests miss reality when environments differ. Add checks that reflect production:
- Run with production-like configuration values.
- Run with realistic data sizes.
- Run with concurrency and timeouts similar to real load.
- Validate that critical logging, tracing, and metrics remain intact.
If your refactor changes performance, treat that as a first-class contract, not a surprise.
A refactoring plan template that stays practical
A refactoring plan becomes useful when it answers a few concrete questions:
- What problem does this refactor solve for users or engineers.
- What is the target architecture in a short module map.
- What safety nets exist today and what must be added.
- What is the commit sequence with verification at each step.
- What is the rollout plan and what is the stop signal.
- What follow-up deletions and cleanup remain after stability.
This is where a plan becomes an engineering instrument instead of a document.
The long-term gain
When spaghetti turns into modules, the system stops demanding heroics. Bugs become easier to isolate. Features become easier to add without breaking unrelated behavior. New engineers can navigate faster. Reviews get sharper because diffs touch fewer concerns at once.
A refactor that ships safely is a form of operational love: it makes the future kinder for the people who will maintain the system and the users who depend on it.
Keep Exploring AI Systems for Engineering Outcomes
Refactoring Legacy Code with AI Without Breaking Behavior
https://ai-rng.com/refactoring-legacy-code-with-ai-without-breaking-behavior/
AI Unit Test Generation That Survives Refactors
https://ai-rng.com/ai-unit-test-generation-that-survives-refactors/
Integration Tests with AI: Choosing the Right Boundaries
https://ai-rng.com/integration-tests-with-ai-choosing-the-right-boundaries/
AI Debugging Workflow for Real Bugs
https://ai-rng.com/ai-debugging-workflow-for-real-bugs/
AI Code Review Checklist for Risky Changes
https://ai-rng.com/ai-code-review-checklist-for-risky-changes/
Books by Drew Higgins
