8 AI Coding Tools, One Messy Codebase: The Refactor Test
We didn’t ask eight AI tools to write new code. We handed each one the same tangled, five-year-old codebase and watched what they did to it. Some cleaned it up. One quietly broke a test suite and called it done.
Article shortcuts: Testing method · The test repos · Tool rankings · Score comparison · Pricing · Verdict by use case · FAQ
01 / why this test existsRefactoring is the job AI coding tools keep getting wrong
Editors keep publishing autocomplete acceptance rates and SWE-bench scores, and none of it answers the question a working developer actually has: if I point this thing at code I already have, will it make things better or worse?
That question has gotten more urgent, not less. Developer surveys and tooling reports through 2024 and 2025 pointed to a genuine paradox: teams shipping more AI-generated code were refactoring it less, and duplicate logic in AI-touched codebases was climbing fast rather than shrinking. The tools built to eliminate technical debt were quietly adding to it.
The most common failure mode is scope, not intelligence. Paste one function into a chat window and the model has no idea how that function is called elsewhere, what invariants the surrounding module depends on, or that an almost-identical helper already exists two folders over. It produces something locally plausible and globally wrong. Repository-wide context — not raw model quality — is what actually separates a refactoring tool from a very confident text generator.
02 / methodologyThe Refactor Integrity Index — how we actually scored these
We built a five-pillar scoring model instead of a single “which one is smartest” ranking, because the tools that felt fastest in a demo were sometimes the ones that broke the most in a real repo. Every tool was scored 1–10 on each pillar by two reviewers independently, then averaged.
Context Depth — does it index the whole repo before touching anything, or just the open file? Structural Coherence — after a multi-file change, does the codebase read like one author wrote it, or does duplicate logic creep back in? Regression Safety — does it write or run tests before claiming success, and did our existing suite still pass? Semantic Precision — when it renames or moves a symbol, does every call site update correctly, including the ones in configs and tests? Economic Efficiency — realistic monthly cost against the time it actually saved, not the list price.
We are not a certified benchmarking lab, and we’re upfront about what that means: this is a hands-on, transparent evaluation by two working developers, not a controlled study with statistical significance. Independent published benchmarks for these same tools disagree with each other by a wide margin — one comparison put a tool’s SWE-bench Verified score at 55%, another cites the same tool nearer 80%, depending on the harness and task set used. We link our sources throughout so you can weigh them yourself rather than take our word — or anyone else’s — as final.
03 / the test bedWhat we actually pointed these tools at
Every tool ran the same four tasks against the same four codebases, in isolated branches, with no tool aware of another’s output. We reviewed every diff by hand before deciding whether it passed.
Legacy Django + Celery monolith
~40,000 lines, five years of accumulated patterns, mixed sync and async task handling. Task: extract duplicated validation logic scattered across three view files into one shared module without breaking any call site.
Node/Express microservices set
Eight services sharing a utilities package that had drifted out of sync. Task: reconcile the duplicated helpers back into the shared package and update every importer.
Java Spring Boot service
Mid-migration from javax to jakarta namespaces. Task: complete the repo-wide namespace migration and fix the resulting dependency and annotation breakage.
React front end
A dashboard suffering from serious prop-drilling technical debt. Task: convert a callback-based data layer to async/await and lift shared state into context without changing the rendered UI.
Every task had one instruction in common: preserve existing behavior, don’t touch unrelated files, and leave the test suite passing. We didn’t pre-clean the repos or remove the awkward edge cases — undocumented branches and messy conditionals are what real legacy code looks like, and we wanted to see whether each tool would ask questions or just plow ahead.
04 / resultsThe rankings
Scores below are our Refactor Integrity Index results from the test bed above — not a restatement of any vendor’s marketing numbers. Where a tool has a well-known public benchmark, we cite it separately in that tool’s notes.
1. Claude Code
AnthropicThe only tool in the test that treated “don’t break anything” as the actual assignment rather than a nice-to-have. It read all four repos before proposing a plan, and on Repo C it stopped mid-migration to flag two annotations it couldn’t verify safely rather than guessing.
Where it won
- Only tool to complete the jakarta migration on Repo C with zero manual fixes
- Wrote and ran characterization tests before touching Repo A’s validation logic
- Largest usable context window of the group, which mattered most on Repo B’s cross-service duplication
Where to watch it
- Terminal-first workflow has a learning curve if your team lives in an IDE
- Token-metered cost adds up fast on very large, repeated multi-file runs
- No local-model option — it needs an Anthropic API key or a supported cloud partner
2. Cursor
AnysphereThe most pleasant day-to-day experience of the eight. Composer’s visual diff review made Repo D’s context-lifting refactor genuinely easy to audit line by line, and its multi-model picker let us route the trickiest step to a stronger reasoning model without leaving the editor.
Where it won
- Best in-editor review experience — every change is a diff you approve, not a black box
- Background agents let us run Repo B’s reconciliation while working on something else
- Model flexibility meant we could swap in a stronger model mid-task when the default stalled
Where to watch it
- Didn’t run our test suite on its own initiative on two of four repos — we had to ask
- Pricing has climbed: Pro+ and Ultra tiers now run well past the old $20 entry point for heavy use
- Composer occasionally proposed a new helper instead of noticing an existing one already covered it
3. JetBrains Junie
JetBrainsThe dark horse of the test. Because Junie sits on top of IntelliJ’s existing structural understanding rather than reading code as plain text, its symbol renames on Repo A were the cleanest of any tool — the same semantic accuracy you’d expect from IntelliJ’s own refactoring engine, just automated.
Where it won
- Symbol-aware renames — no other tool matched its call-site accuracy on Repo A
- Runs syntax and semantic checks automatically before presenting a change
- Free tier is genuinely usable for occasional refactors, not just a trial
Where to watch it
- Locked to JetBrains IDEs — no benefit if your team is on VS Code
- Weaker on cross-language, cross-service work like Repo B’s Node reconciliation
- Free tier’s 3 monthly AI credits run out almost immediately on real work
4. Windsurf
CognitionCascade’s autonomous multi-step planning made short work of Repo D’s async conversion, moving fast through the mechanical parts. It was noticeably less careful on the parts that required judgment calls, twice needing a second pass after we flagged a broken edge case.
Where it won
- Fastest raw execution speed on mechanical, well-bounded refactors
- Local codebase indexing kept it reasonably grounded across thousands of files
- Strong IDE plugin coverage beyond VS Code, including JetBrains and Neovim
Where to watch it
- Needed a second corrective pass on two of four test repos
- Pricing has changed twice in 2026 — confirm the current tier before budgeting
- Less conservative than Claude Code or Junie about flagging risky changes
5. GitHub Copilot
GitHub / MicrosoftCopilot Workspace is genuinely good at converting a well-scoped GitHub issue into a pull request, and its multi-file edit mode has come a long way from pure autocomplete. On repo-wide, loosely-specified refactors, it consistently narrowed its own scope more than we asked it to.
Where it won
- Best value in the group if your team is already deep in GitHub workflows
- Coding agent turns a well-defined issue into a reviewable PR with minimal setup
- Genuinely useful free tier — the only one of the eight with no real trial ceiling
Where to watch it
- Underperformed the other agentic tools on Repo B’s cross-service reconciliation
- Tends to complete the literal request and stop, missing adjacent duplication
- Best treated as a strong autocomplete-plus-PR tool, not a primary refactoring engine
6. Sourcery
Sourcery AISourcery doesn’t try to be an agent, and that narrowness is exactly its value. Pointed only at Repo A’s mechanical patterns — dead code, redundant conditionals, non-idiomatic Python — it produced the cleanest, most surgical diffs of any tool in the test. It has no meaningful answer for Repo B or Repo C.
Where it won
- The single most idiomatic, minimal diffs of the whole test on mechanical clean-up
- Runs automatically in CI/PR review, catching drift before it merges
- Cheap and predictable relative to agentic, token-metered tools
Where to watch it
- Not built for repo-wide or cross-service refactors — that’s simply outside its scope
- Python and a handful of other languages only; no help on Repo C’s Java migration
- Best used alongside an agentic tool, not instead of one
| Also tested | Where it’s strong | Handled our multi-file refactors? |
|---|---|---|
| Gemini Code Assist | Unit test generation, security-vulnerability scanning, deep Google Cloud IDE integration | Partially — best on Repo D, weaker elsewhere |
| CodeScene | Flags complexity hotspots and code-health trends before you refactor anything | No — it’s an analysis layer, not an editor |
| Tabnine | Privacy-first, self-hosted deployment for regulated teams | Partially — solid completions, cautious on large diffs |
05 / at a glanceScore comparison across the top four
Refactor Integrity Index scores, our test bed, July 2026 — not a third-party benchmark
06 / costPricing, honestly (it moves fast)
Every tool in this space changed its pricing structure at least once in 2026. Windsurf alone moved from a credit system to daily quotas in March, then raised its Pro tier again in May. Treat every number below as a snapshot from our testing window, and confirm the live price before you commit a budget line.
| Tool | Entry price | Power-user tier | Team / enterprise |
|---|---|---|---|
| Claude Code | $20/mo (Pro) | Max plans, usage-based API | ~$20–25/seat/mo, capped at 150 seats |
| Cursor | $20/mo | Pro+ $60/mo, Ultra $200/mo | Custom, roughly double Copilot’s per-seat cost |
| JetBrains Junie | Free (3 credits/mo) | Pro $10/mo | Ultimate $30/mo (IDE license separate) |
| Windsurf | Free tier | Pro ~$20/mo | Teams ~$25/seat/mo, Max $200/mo |
| GitHub Copilot | Free tier | Pro $10/mo, Pro+ $39/mo | Enterprise $39/seat/mo |
| Sourcery | Free tier | Paid individual plan | Team plans, billed per seat |
07 / the verdictWhich one should you actually install?
Repo-wide refactor, migration, or architecture change: Claude Code was the only tool that didn’t need a second, corrective pass across all four of our test repos. If the job is “understand this whole system and change it correctly,” it earned that role.
Daily-driver editor with visible, reviewable diffs: Cursor. Composer’s review workflow made it the fastest to trust for smaller, everyday changes, even when its multi-file judgment lagged Claude Code’s on the harder tasks.
Locked into a JetBrains stack: Junie punches well above its price for Java, Kotlin, and Python teams who already live in IntelliJ — its symbol-aware renames were the most reliable of the group.
Budget-constrained or already deep in GitHub workflows: Copilot remains the best value if your team’s process already runs through GitHub issues and PRs, as long as you don’t expect it to own large, ambiguous refactors alone.
Python-only, mechanical clean-up at scale: Sourcery is worth running continuously in CI even if you also use an agentic tool — it catches the small stuff the bigger tools skip past.
08 / faqCommon questions
Is AI refactoring safe for production code?
Only with the same discipline you’d apply to a human contributor’s PR: characterization tests before the change, a reviewed diff, and a rollback plan. Every tool in this test produced at least one change we would not have merged unreviewed.
What’s the difference between AI code refactoring and AI code generation?
Generation writes new code against a spec with no prior constraints. Refactoring has to preserve exact existing behavior while changing structure — which means a tool can be excellent at one and mediocre at the other, as several tools here were.
Do I need an agentic tool, or is a plugin like Sourcery enough?
Depends on the job. Mechanical, single-language clean-up runs fine on a narrow tool like Sourcery. Anything crossing files, services, or languages needs repository-level context, which only the agentic tools in this test provided.
Why do published benchmark scores for the same tool vary so much between sources?
Different articles run different task sets, different agent scaffolds, and different model versions under the same tool name. That’s a real limitation of the current benchmarking landscape, not just a typo — cross-check any single number against at least one other source before trusting it.
further readingMore from Code Talent Hub
sourcesExternal references
NxCode: Cursor vs Claude Code vs GitHub Copilot 2026 · Cosmic: Full pricing comparison · Tech Syntax: SWE-bench figures · CloudZero: Windsurf pricing history · DEV Community: duplicate-code growth data · The CTO Club: 2026 refactoring tools list