A practical checklist for evaluating AI coding assistants, IDE copilots, and agentic coding workflows.
This repo is maintained as a small public reference for teams comparing tools for code completion, code review, refactoring, and repository-aware automation. For a browsable comparison hub, see AI Coding Hub.
Use the checklist below when testing an AI coding tool against real repository work.
| Area | What to test | Why it matters |
|---|---|---|
| Repository context | Can the tool follow project structure, conventions, and tests? | Better context usually means fewer generic edits. |
| Multi-file changes | Can it update related files without breaking contracts? | Real coding work often spans components, tests, and docs. |
| Debugging loop | Can it run commands, inspect failures, and iterate? | The loop is where productivity gains show up. |
| Review quality | Does it catch regressions and missing edge cases? | Good code review support reduces risk. |
| Security posture | Does it avoid leaking secrets or making unsafe changes? | AI coding tools need clear safety boundaries. |
- Pick a small issue from a real project.
- Ask the tool to inspect the code before editing.
- Require a focused patch and a test run.
- Review whether the final change matches local style.
- Track time saved, number of corrections, and test reliability.
- AI coding comparison hub: AI Coding Hub
- Public docs site for this repo: https://cooldk.github.io/ai-coding-tools-guide/