Code Migration with AI

Migrate code across frameworks/languages with a mapping table + per-file verification.

Docs & RefactorAdvanced

Why

GitHub's 2024 survey found 81% of developers expect AI tools to become more integrated in documenting code over the next year, the highest of any workflow. Migrations (JS→TS, Express→Hono, REST→tRPC) are mechanical but error-prone.

AI with an explicit old→new mapping table does the mechanical part; you verify each file with tests.

When

Use for framework/language migrations with a clear mapping. Skip for paradigm shifts needing redesign.

How

1. Build an old→new mapping table (API, patterns, types). 2. Prompt: "migrate this file per the table; preserve behavior; flag anything not in the table". 3. Migrate one file per commit; run tests after each. 4. For unmapped patterns: decide manually, add to the table. 5. Run the full gate at the end.

Example

Mapping: Express → Hono app.get(path, handler) → app.get(path, (c) => handler(c.req, c)) req.body → c.req.json() res.json(x) → c.json(x) Prompt: "migrate routes/users.ts per the table; preserve behavior; flag anything unmatched." Migrate one route file per commit; tests gate each.

Common Problems

⚠Error: Cannot find module 'xxx' after refactor → re-run `npm install` and `npx tsc --noEmit`; refactors often drop or rename imports.
⚠Error: "Cannot read properties of undefined" after refactor → run the test suite (`npm test`) to catch behavior changes; refactor must be behavior-preserving.
⚠Error: Docs drift: example no longer runs → execute examples in CI (`npm run docs:check`); tested examples never drift.

Best Practices

✓Build the mapping table first; migrate per file with tests gating each.
✓Flag anything not in the table rather than guessing.
✓Measure the outcome with a concrete metric (time saved, defect rate, test coverage) so the practice can be justified and improved.

FAQ

What is Code Migration with AI?

Migrate code across frameworks/languages with a mapping table + per-file verification. It helps developers quickly grasp the core value and use cases of Code Migration with AI.

How do I get started with Code Migration with AI?

To get started with Code Migration with AI, focus on these points first: Build an old→new mapping table (API, patterns, types).

What are common mistakes with Code Migration with AI?

Common mistakes when using Code Migration with AI include: Error: Cannot find module 'xxx' after refactor → re-run `npm install` and `npx tsc --noEmit`; refactors often drop or rename imports.; Error: "Cannot read properties of undefined" after refactor → run the test suite (`npm test`) to catch behavior changes; refactor must be behavior-preserving.; Error: Docs drift: example no longer runs → execute examples in CI (`npm run docs:check`); tested examples never drift..

What are the best practices for Code Migration with AI?

Best practices for Code Migration with AI include: Build the mapping table first; migrate per file with tests gating each.; Flag anything not in the table rather than guessing.; Measure the outcome with a concrete metric (time saved, defect rate, test coverage) so the practice can be justified and improved..

How do I learn more about Code Migration with AI?

Start with the official docs, then search real-world examples on GitHub. Validate patterns in a small scratch repo before applying them to production code.