$_ CodeCosts

Code migration is one of the hardest problems you can throw at an AI coding tool — and the results expose the gap between tools that understand code transformation and tools that merely autocomplete syntax. The easy part of any migration is the syntax: renaming a function call, swapping an import path, changing a method signature. Any LLM can do that. The hard part is everything else: preserving behavior across thousands of edge cases, handling breaking API changes that require architectural rewrites, updating test suites that relied on the old behavior, migrating configuration files that changed format between versions, and ensuring the new code actually produces identical outputs to the old code in production.

We tested every major AI coding tool on real migration tasks — upgrading React class components to hooks, porting Python 2 codebases to Python 3, migrating Java 8 to Java 17, upgrading Spring Boot across major versions, converting REST APIs to GraphQL, and decomposing monoliths into service boundaries. The results were stark: most tools can transform individual files in isolation, but only a few can coordinate changes across an entire codebase, track dependency chains through breaking changes, and — most critically — verify that the migration preserved the original behavior by running tests and comparing outputs.

What Makes Code Migration Different for AI Tools

Code migration is fundamentally different from writing new code. When you write new code, the AI only needs to produce something that works. When you migrate existing code, the AI must produce something that works identically to the original — while changing every internal detail. This creates a unique set of challenges that separate capable migration tools from useless ones:

• Syntax transformation vs semantic preservation: Changing componentDidMount to useEffect is syntax. Ensuring the effect runs at the same lifecycle point, with the same cleanup behavior, and the same dependency tracking is semantics. Most AI tools nail the syntax and silently break the semantics. A migration that compiles but changes behavior is worse than no migration at all — you ship bugs that pass code review because the code “looks right.”
• Breaking API change handling: Framework upgrades don’t just rename functions — they remove entire paradigms. React dropped mixins. Django removed url() in favor of path() with different regex syntax. Spring Boot 3 moved from javax.* to jakarta.* namespaces, breaking every import in every file. The AI tool needs to understand not just what changed, but the intent behind the change and the new idiomatic way to achieve the same result.
• Dependency graph awareness: Changing a function signature in one file breaks every call site in every other file. Renaming a database column requires updating the model, every query, every serializer, every test fixture, and every migration script. An AI tool that migrates files independently will produce a codebase that does not compile, let alone run.
• Test migration and creation: The existing test suite was written for the old API. After migration, tests need to change to use the new API while still asserting the same behavior. Often the test infrastructure itself needs migration — moving from unittest to pytest, or from Enzyme to React Testing Library. And if the codebase had no tests, the migration tool ideally generates characterization tests before migrating, so you can verify nothing changed.
• Configuration file updates: Framework upgrades often change configuration formats. Webpack 4 to 5 changed plugin APIs. Docker Compose v2 changed the YAML schema. Kubernetes deprecated entire API versions. These config changes interact with application code in non-obvious ways — a build config that references a removed plugin will fail silently or with cryptic errors.
• Gradual migration support: Real-world migrations happen incrementally. You cannot stop feature development for six months while you port 500,000 lines of code. The AI tool needs to support mixed states — old and new code coexisting, adapter layers bridging the gap, feature flags controlling which code path runs. Tools that only understand “migrate everything at once” are impractical for any codebase above toy scale.
• Rollback safety: When a migration goes wrong (and it will), you need to revert cleanly. This means the migration should produce atomic, reviewable changesets — not one giant commit that touches every file. The AI tool should produce changes that can be merged in phases, with each phase independently deployable and revertable.

Migration Feature Comparison

We evaluated each tool on the core capabilities that determine migration success. Star ratings reflect real-world migration quality, not marketing claims:

Tool-by-Tool Breakdown

Claude Code — Best for Verified Migrations

Claude Code is the standout for code migration because it does something no other tool does reliably: it runs your test suite after making changes and iterates until the tests pass. This is the difference between “I transformed the syntax” and “I verified the migration preserved behavior.” When you give Claude Code a migration task, it reads the existing code, makes the changes, runs pytest or npm test or mvn verify, sees the failures, reads the error messages, fixes the issues, and repeats until the suite is green. This closed-loop approach catches the subtle behavioral regressions that every other tool misses.

Strengths: Strongest behavior verification through test execution. Handles multi-step migrations where each step depends on the previous one. Excellent at reasoning through breaking API changes and finding the idiomatic replacement. Can generate characterization tests before migrating when no tests exist. Works across the full codebase from the terminal without IDE constraints.

Weaknesses: Terminal-only interface has a learning curve. No visual diff preview before applying changes. Token-intensive for large migrations — the Max plan ($200/mo) may be needed for 100k+ line codebases. Slower than tools that just transform without verifying.

Best for: Migrations where correctness matters more than speed — financial systems, healthcare, infrastructure code. Any migration where “it compiles” is not an acceptable bar.

See Claude Code pricing →

Cursor — Best Multi-File Migration Coordination

Cursor’s Composer mode is purpose-built for the kind of coordinated, multi-file changes that migrations require. You describe the migration task, Composer reads the relevant files, produces a unified diff across all of them, and lets you review every change before accepting. For framework upgrades that touch dozens of files — renaming imports, updating method signatures, fixing call sites — Composer’s ability to see and edit multiple files simultaneously produces more consistent results than tools that operate file-by-file.

Strengths: Multi-file Composer mode handles coordinated changes across 20+ files at once. Inline diff view lets you review every migration change before accepting. Model-agnostic — you can use Claude, GPT, or Gemini depending on the migration type. Codebase indexing ensures the tool sees your full dependency graph. Tab-completion learns your codebase’s naming conventions and applies them consistently during migration.

Weaknesses: No automatic test execution after migration — you must manually run tests and feed failures back. Context window limits mean very large files may be truncated during analysis. Composer can sometimes produce inconsistent changes when the migration exceeds its context budget.

Best for: Framework upgrades that require coordinated changes across many files. Teams that want visual review of every change before accepting.

See Cursor pricing →

Amazon Q Developer — Best for Java Migrations

Amazon Q Developer has purpose-built migration agents that no other tool matches for Java workloads. The Java upgrade agent handles Java 8 to 17 migration with awareness of removed APIs (javax.xml.bind, java.security.acl), module system requirements, and deprecated patterns. It also handles Spring Boot 2 to 3 upgrades including the javax to jakarta namespace migration, updated Spring Security configuration patterns, and revised property names. These are not generic LLM prompts — they are specialized transformation pipelines built on Amazon’s analysis of millions of Java codebases.

Strengths: Purpose-built Java 8→17 upgrade agent handles the full scope of API removals and replacements. Spring Boot upgrade support covers namespace migration, security config changes, and property renames. Free tier includes migration functionality — no paid plan required for basic upgrades. Understands Maven and Gradle build file changes needed alongside code migration. Handles .NET Framework to .NET Core transformations for C# shops.

Weaknesses: Java-centric — migration quality drops significantly for non-Java/Python languages. Limited multi-file coordination for languages outside its specialty. IDE plugin only (VS Code, JetBrains) — no terminal-based option. Less effective for unconventional project structures that deviate from Maven/Gradle conventions.

Best for: Enterprise Java migrations — Java 8 to 17, Spring Boot 2 to 3, Jakarta EE transitions. Organizations with large Java codebases that need structured, repeatable migration support.

See Amazon Q pricing →

GitHub Copilot — Decent Syntax Transformations

Copilot handles straightforward, file-level syntax transformations competently. If you need to convert a Python 2 print statement to a function call, or update a deprecated API to its replacement, Copilot’s inline suggestions will get you there. The Copilot Chat interface can explain what changed between framework versions and suggest the migration path. But Copilot struggles with migrations that span multiple files or require understanding the broader codebase context — it operates on the current file and its immediate imports.

Strengths: Inline suggestions during manual migration are fast and accurate for simple transformations. Chat mode explains deprecation warnings and suggests modern replacements. Works in VS Code, JetBrains, Neovim — broadest editor support. Free tier (2,000 completions/mo) is sufficient for small migration projects. Strong knowledge of popular framework upgrade paths (React, Django, Rails, Express).

Weaknesses: Single-file context means it misses cross-file dependency breakage. No automatic test execution or verification. Chat-based migration suggestions require manual application — no automated multi-file apply. Cannot reason about the codebase as a whole — struggles when migration requires understanding custom abstractions built on top of the framework.

Best for: Small migrations under 20 files. Developers who prefer manual control and want AI assistance, not AI automation.

See Copilot pricing →

Gemini Code Assist — Largest Context for Massive Migrations

Gemini Code Assist’s defining advantage for migration is context window size. With 1 million tokens of context, Gemini can ingest your entire codebase — every source file, test, configuration, and build script — and reason about migration changes with full awareness of every dependency. For large codebases where other tools hit context limits and start missing cross-file references, Gemini maintains coherence. You can paste an entire module (50+ files) and ask for a comprehensive migration plan with changes ordered by dependency.

Strengths: 1M token context window handles codebases that overflow every other tool’s limits. Full codebase awareness reduces missed dependencies during migration. Free tier is generous enough for substantial migration projects. Strong at generating migration plans that order changes by dependency graph. Good at identifying all files affected by a breaking change across the codebase.

Weaknesses: IDE integration is less polished than Cursor or Copilot — applying suggested changes is more manual. No automatic test execution or behavior verification. Large context can cause slower response times compared to tools with smaller context budgets. Sometimes produces overly verbose migration plans that need trimming.

Best for: Large-scale migrations (100k+ lines) where context window is the bottleneck. Teams that need a comprehensive migration impact analysis before starting.

See Gemini pricing →

Windsurf — Migration with Cascade Assistance

Windsurf’s Cascade agent can handle multi-step migration tasks by breaking them into sequential operations. You describe the overall migration goal, Cascade creates a plan, and executes each step with awareness of the previous steps’ results. For framework upgrades that follow a predictable pattern (update dependencies, fix imports, update API calls, fix tests), Cascade’s sequential execution model works well. It reads files, makes changes, and moves to the next file with context from what it already changed.

Strengths: Cascade agentic mode handles sequential, multi-step migration workflows. Retains context between steps — changes in step 3 reflect awareness of changes in steps 1 and 2. Free tier includes agentic functionality for trying migration workflows. File-aware context means Cascade reads the files it needs without manual @-mentions.

Weaknesses: No automatic test execution to verify migration correctness. Cascade can lose coherence on long migration sessions that exceed its effective context. Less control over which files are modified compared to Cursor’s explicit Composer file selection. Credit-based pricing on paid tiers makes cost unpredictable for large migrations.

Best for: Medium-sized framework upgrades where you want step-by-step execution with visual review. Teams exploring agentic migration workflows without commitment to a paid plan.

See Windsurf pricing →

Sourcegraph Cody — Codebase-Wide Pattern Transformations

Cody’s integration with Sourcegraph’s code graph gives it a unique advantage for migrations that require finding every instance of a pattern across a massive codebase. If your migration involves replacing every call to a deprecated function with its successor, Cody can search the entire repository (or multiple repositories) and apply the transformation everywhere. This is particularly valuable for monorepo migrations where changes must be coordinated across dozens of packages.

Strengths: Codebase-wide search finds every instance of a deprecated pattern, including in files you forgot existed. Multi-repo support handles migrations that span multiple repositories (monorepo or polyrepo). Strong at consistent pattern application — if the replacement for oldApi() is newApi(config), Cody applies it uniformly everywhere. Context from the full code graph reduces false positives in search-and-replace style migrations.

Weaknesses: Requires Sourcegraph infrastructure setup for full code graph benefits. Pattern-based approach is less effective for migrations that require per-instance reasoning (each call site needs a different replacement). No automatic test execution or behavior verification. Enterprise pricing can be significant for small teams.

Best for: Large organizations with monorepos or many repositories. Migrations that are primarily consistent pattern replacements across a large codebase.

See Cody pricing →

Tabnine — Convention-Aware Migration Suggestions

Tabnine’s strength is learning your codebase’s specific conventions and applying them during migration. If your team wraps every API call in a custom error handler, Tabnine learns that pattern and applies it when suggesting migrated code. This convention awareness prevents the common problem where AI-migrated code is technically correct but stylistically inconsistent with the rest of the codebase. However, Tabnine’s migration support is limited to inline suggestions — it does not have agentic capabilities for multi-step migration workflows.

Strengths: Learns codebase conventions and applies them during migration — migrated code matches your style. Privacy-focused — runs locally or in your own infrastructure, relevant for codebases with compliance requirements. Inline suggestions during manual migration are context-aware and respect your patterns. Enterprise features include code governance policies that can enforce migration standards.

Weaknesses: No agentic mode — cannot autonomously execute multi-file migrations. No test execution or behavior verification. Weakest migration capability of all tools compared here — limited to suggestion-level assistance. Cannot generate migration plans or coordinate changes across files.

Best for: Teams doing manual migrations who want AI-assisted completions that match their coding conventions. Organizations with strict code privacy requirements.

See Tabnine pricing →

Common Migration Tasks — Best Tool for Each

The Correctness Verification Factor

This is the factor that separates tools that help with migration from tools that complete migration. And it is the single most important differentiator we found in our testing.

Most AI coding tools approach migration as a text transformation problem: read the old code, produce the new code, done. This is the same mental model as a find-and-replace script, just with a smarter pattern matcher. The result is code that looks migrated — the imports are updated, the method names are changed, the new API patterns are used — but nobody checked whether it actually works the same way as the original.

The correctness verification gap is enormous in practice. In our testing, even the best AI tools produced migrated code that compiled and looked correct but contained subtle behavioral regressions in 15–30% of non-trivial migrations. A React class component with complex shouldComponentUpdate logic gets converted to a React.memo with an incomplete comparison function. A Python 2 dict.keys() call that relied on returning a list gets migrated to Python 3 where it returns a view — which breaks when the dictionary is modified during iteration. A Spring Security configuration that used WebSecurityConfigurerAdapter gets converted to the new SecurityFilterChain pattern but loses a custom authentication provider in the translation.

Only two tools in our comparison actually close this loop:

• Claude Code runs your test suite after making migration changes, reads the failure output, diagnoses what went wrong, fixes the code, and re-runs the tests. This cycle repeats until either the tests pass or Claude identifies a problem that requires human judgment. In our testing, Claude Code caught and fixed 80%+ of behavioral regressions that other tools silently introduced.
• Amazon Q Developer (for Java specifically) uses static analysis and compilation checks as part of its migration pipeline. It does not run your full test suite, but it verifies that the migrated code compiles, resolves all imports, and passes its own transformation validation rules. For Java’s type system, this catches a significant class of regression.

Every other tool in this comparison produces a migration diff and leaves verification entirely to you. This does not mean they are useless — Cursor’s visual diff review and Gemini’s full-codebase awareness both reduce the chance of errors. But they are fundamentally “transform and hope” tools, not “transform and verify” tools. For any migration where correctness is non-negotiable (financial systems, healthcare, infrastructure), the verification loop is the entire ballgame.

Bottom Line

Methodology

We tested each tool on real-world migration tasks across multiple languages and frameworks: React 16→18 (class to hooks, new root API), Django 3→5 (async views, URL configuration), Spring Boot 2→3 (Jakarta namespace, Security DSL), Python 2→3 (unicode handling, print functions, dictionary methods), and Java 8→17 (module system, removed APIs, records, sealed classes). Each tool was evaluated on correctness (did the migration preserve behavior?), completeness (did it find all affected files?), and effort (how much manual cleanup was required?). Pricing reflects published rates as of March 2026.

Related Guides

Pricing and feature data sourced from official vendor pages as of March 2026. We update this guide as tools change. Found an error? Open an issue.