May 16, 2026

Refactoring with AI: A Real-World Case Study (2026)

By AICoderScope Team · 11 min read

cursorclineaidercopilotworkflowrefactoringreview

Two refactoring jobs. One took 8 minutes to plan and 22 minutes to execute — work that would have taken an afternoon manually. The other ate 90 minutes of tool fights, hallucinated imports, and one partial rewrite that had to be reverted. Both used Cursor Agent mode. The difference was scope, architectural complexity, and one critical mistake in how context was fed to the model.

This is what AI-assisted refactoring actually looks like in 2026.

The two scenarios

Scenario A: Multi-file parameter cleanup — Remove two deprecated parameters ('new-ui-design': true in feature flag configs and shouldClip: true in snapshot calls) across 64 Playwright test spec files. Mechanical work: no architecture decisions, just consistent application of a rule across a large file set.

Scenario B: Architectural migration — Migrate 1,200 lines of Express.js route handlers into Next.js Server Actions. Files ranged from 80 to 300 lines each. Shared middleware, database queries, authentication checks, and custom types scattered across a dozen files. The kind of refactor that typically consumes 2–3 developer days.

These two scenarios represent opposite ends of the refactoring spectrum. The tools that handle one well don’t necessarily handle the other.

Cursor: strong on both, with conditions

Scenario A

Cursor’s Composer (Agent mode) handles the 64-file cleanup — but only if you split the task. A single prompt of “remove both deprecated parameters from all 64 files” produced inconsistent results with occasional unintended edits in a real-world case documented by a developer on DEV Community. The fix: two sequential tasks. Snapshot cleanup first (shouldClip: true), then feature flag cleanup. This avoids overlapping edits and keeps each pass verifiable via git diff.

Composer has a 25-task execution limit before requiring manual confirmation. For 64 files across two passes, you’ll hit that checkpoint once — it’s one click, not a real obstacle.

Total time: ~22 minutes (two pass prompts + git diff review). Manual estimate for the same work: 2–3 hours.

Scenario B

The three-agent pattern documented with Cursor Subagents — Researcher (dependency mapping) → Tester (existing test coverage) → Builder (implementation) — ran sequentially in roughly 18 minutes for the first batch of routes. Use Plan mode (Shift+Tab) before Agent mode so you see the full list of affected files before any edits land. That preview is worth more than it looks when you’re touching shared auth logic.

The context ceiling showed up at around 8 files per session. Beyond that, the Builder agent started duplicating authentication middleware — including auth checks in route files that were supposed to import them from a shared module. A post-run grep -r "authMiddleware" src/ caught 3 duplicates across 11 route files. Two minutes to find, five minutes to fix.

Total time: 28 minutes (plan review + two batched agent sessions + manual dedup). Manual estimate: 3–4 hours.

Pricing note: Agent mode requires Cursor Pro at $20/month. The Hobby (free) tier has hard limits on Agent requests that make multi-file work impractical.

GitHub Copilot: fastest on mechanical changes, weaker on architecture

Scenario A

For the 64-file parameter cleanup, Copilot’s multi-file Agent mode is the fastest option if you’re already in VS Code. A single intent prompt — “remove all instances of shouldClip: true from snapshot calls across the test directory” — and Agent mode generates a plan, shows you affected files, and applies the diff across the repo. On the Pro tier ($10/month), agent mode runs with unlimited requests using GPT-5 mini as the base model; higher-tier models cost premium request credits.

The diff preview before application is solid. For purely mechanical changes where you can visually scan 64 diffs in 3 minutes and confirm, this is the winner on speed.

Scenario B

Agent mode handled route-by-route migration with reasonable accuracy, but it consistently misread the shared authentication middleware — inlining its logic rather than importing it. Three of eight migrated routes required manual correction. Copilot’s inline chat (Ctrl+I / Cmd+I) is genuinely useful for cleanup prompts after the migration runs, but the upfront architectural reasoning is noticeably weaker than Cursor’s Plan mode for complex multi-file restructuring.

Copilot is the right tool when you can describe the change as a search-and-replace rule. When the change requires understanding how modules relate to each other, it struggles.

Cline: slower, safest for production code

Scenario A

Cline’s step-by-step approval model adds friction to the 64-file cleanup — each file modification requires a confirmation. For developers who don’t know the codebase well, that approval loop catches model drift. For developers who know the code cold and want speed, it slows you down relative to Cursor or Copilot.

Scenario B

Cline’s Plan/Act separation was the most controlled approach for the architectural migration. Break the task explicitly: “extract the user-routes module into its own service, preserve these three imports, do not moDify the auth module.” Cline v3.82+ honors .clinerules constraints declared before the refactor runs — you can mark files or directories as off-limits.

This is the right tool when you’re touching production-critical code where a bad edit isn’t just annoying — it’s a 2am incident. The tradeoff: API costs. Heavy use of Claude Sonnet 4.6 through Cline for a 1,200-line migration ran approximately $4–6 in API fees during testing. That’s not prohibitive, but it’s real.

Cross-link: Cline + Local LLM Privacy-First Setup 2026 covers running Cline with a local model to eliminate API costs when privacy or budget matters.

Aider: best Git hygiene, best for terminal workflows

Scenario A

Aider’s --architect mode separates the reasoning model (which plans changes) from the edit model (which applies them). For the 64-file cleanup:

aider --architect --model claude-sonnet-4-6 tests/**/*.spec.ts

The repo-map feature built a condensed structural overview automatically — no manual file selection. Each change landed as a discrete Git commit, making it trivially reversible if one file got the wrong parameter stripped. Aider’s diff format reduced editing errors by approximately 30% compared to search-and-replace methods on complex patterns, according to 2026 benchmark comparisons.

Scenario B

Architect mode with --watch was the most reliable approach for avoiding unintended changes during the Express-to-Next.js migration, but it requires you to write explicit task descriptions for each batch. There’s no visual diff before execution — changes land in the terminal in patch format. If you’re not comfortable reviewing patches, the learning curve is real. If you live in the terminal already, the clean commit history alone makes this worth the overhead.

git log --oneline after an Aider-assisted migration shows exactly which route moved in which commit. That’s invaluable if something breaks in QA.

Cross-link: Aider with Local LLM via Ollama 2026 covers the setup if you want to run Aider without API costs.

The context window is the real constraint

Every AI refactoring session has a ceiling. The advertised context window and the effective context window are not the same number.

Models claiming 200K tokens typically degrade in quality around 130K — not gradually but suddenly, with performance drops that are hard to predict in advance. In practice, the safe upper bound for a refactoring session is around 8–12 complex files before you should start a fresh context. Feeding an entire src/ tree to the model and expecting coherent edits is the most common mistake.

Three approaches that keep you under the ceiling:

1. Batch by dependency group. Auth routes in one session, data routes in another. Group files by what they share, not by folder structure.

2. Write a handoff file. After each agent session, ask the model to write a refactor-status.md: what’s done, what’s pending, which types and interfaces changed. The next session reads this file as context priming rather than needing the full codebase. This cuts context load by 60–70% on day-two sessions.

3. Pre-filter with grep. Run grep -r "shouldClip" src/ before the agent session so you feed it only the 64 relevant files, not everything under src/. Cursor’s Plan mode approximates this automatically. Aider’s repo-map does it structurally. For Copilot and Cline, you do it manually.

Where AI refactoring actually breaks

Shared state mutations. When multiple files read and write the same mutable object — global config, singleton class, Redux store — models frequently introduce duplicate writes or forget to update consumers. This is the most common source of “it looked right but broke at runtime.”

Framework idioms it doesn’t know. The Express-to-Next.js migration produced three silent failures where the model used Express-style req.body patterns instead of Next.js Server Action argument binding. No linter catches this. Only tests do — which means you need tests before the refactor, not after.

Cascading renames. Rename a shared utility type and you need every importer updated. Cursor Agent and Copilot Agent both handle this reasonably well today. Cline handles it only within the files you explicitly scope. Aider handles it via repo-map, which is nearly automatic but not perfect on deeply nested barrel exports.

Files over ~500 lines. Once a single file exceeds around 500 lines, model accuracy on “change only this block, leave everything else alone” degrades noticeably. The fix is always the same: break the file before the refactor, not during it.

The first-pass success rate across complex refactoring sessions on clean codebases runs around 75–85%. That means roughly 1 in 6 sessions produces at least one silent bug. A post-run git diff review is not optional.

Honest take

For mechanical multi-file changes — removing deprecated params, updating import paths, renaming constants across a repo — Copilot Agent mode in VS Code is the fastest path. One intent prompt, automatic diff, done.

For architectural migrations — framework swap, service extraction, module restructuring — Cursor with Plan mode gives the best balance of autonomous execution and pre-flight visibility. Commit at every batch. The Pro tier at $20/month pays for itself on the first refactor of any real size.

For production-critical code where a bad edit causes an incident — Cline with .clinerules constraints gives you the tightest control loop. It’s slower and costs API fees, but you approve every change before it lands.

For terminal-native workflows and the cleanest Git history — Aider in architect mode is the most cost-efficient approach for batch changes where you know exactly what needs to change.

None of these tools replace reading the diff. A 15% silent failure rate is low enough to be impressive and high enough to bite you badly if you skip verification.

Refactoring type	Best tool	Time vs manual	Risk level
Multi-file parameter / string removal	Copilot Agent	~70% faster	Low
Architectural migration (framework, service)	Cursor Pro (Plan + Agent)	60–75% faster	Medium
Production-critical changes, unfamiliar codebase	Cline + .clinerules	40–50% faster	Low
Terminal workflow, batch changes	Aider —architect	~65% faster	Low–Medium

Related reading

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Last updated May 16, 2026. Pricing and features change frequently; verify current state before purchasing.

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