Software Engineering

Refactoring large codebases

Use AI to propose and implement refactors across larger codebases, including structural and architectural changes.

Why the human is still essential here

The engineer decides the refactor strategy, reviews diffs, runs tests, and confirms behavior/performance remain correct; AI suggestions must be validated.

How people use this

Bulk rename and API migration

AI updates call sites across the repo to match a renamed API or signature change while keeping formatting and imports consistent.

Claude Code / Sourcegraph Cody

Extract shared libraries

AI identifies duplicated logic across services and proposes a shared module/library extraction with incremental PR-sized changes.

Claude Code / GitHub Copilot Chat

Monolith to layered architecture refactor

AI helps restructure a monolith into clearer layers (domain/service/adapters), generating diffs that engineers review and test thoroughly.

Claude Code / JetBrains AI Assistant

Community stories (1)

I've been using Claude Code daily for 6+ months.

I've been using Claude Code daily for 6+ months. It's replaced most of my routine work:
• To understand code base - yes true this is game changing for me. its creates nice tech documents.
• Boilerplate generation
• Refactoring large codebases
• Debugging edge cases
• Writing tests & automation scripts - top notch in UT's
It outperforms Cursor and GitHub Copilot for deep reasoning and architectural changes. Period.
⚠️ BUT — always validate. Review the code. Run your tests. You're still the final gatekeeper. AI hallucinates less here, but "less" ≠ "never."

SpR

Sai prathap ReddyStaff Engineer @ServiceNow

Feb 23, 2026