A published coding score is a starting point for a shortlist. Your codebase adds its own requirements: conventions, dependencies, deployment limits, accessibility, maintainability and the cost of review. Run a small comparison on work you understand before adopting a tool across a team.
1. Choose tasks with a checkable finish
Choose a few bounded tasks from an isolated or synthetic project: a bug with a reproducible failure, a change to an existing feature, a small refactor and a case involving incomplete requirements. Give each a written acceptance condition. Preserve a clean starting point so each candidate gets the same code and context.
Do not use production credentials or unapproved customer data. Use a branch or disposable working copy. Decide whether the assistant may run commands, install packages or access the network. These permissions affect the workflow and belong in the comparison record.
2. Keep the comparison record complete
- Record model, agent, tool version and relevant settings.
- Keep the starting revision, task wording and allowed context fixed.
- Preserve the code diff and any commands the tool executed.
- Record generated output, failed attempts, review time and corrections.
- State whether the assistant saw the acceptance checks.
Use checks written independently of the generated solution. A test that repeats the same mistaken assumption can pass with an incorrect implementation. Inspect the actual behavior and code changes as well.
Worked example: an accessible mobile menu
This is a proposed test, not a measured tool result. Ask the assistant to fix a menu that cannot be closed with a keyboard. Acceptance checks include opening with a keyboard, Escape closing it, focus returning to the toggle, and the hidden menu leaving the tab order. Also inspect narrow-screen layout and text resizing. “The page loads” is not enough to accept the change.
3. Count the work left for a person
Record total time from task start to an accepted result, including reading the diff, repairing defects and verifying the change. Report abandoned attempts too. If one tool generates a solution quickly but requires a long review, that is part of its cost for this task.
For web changes, W3C’s accessibility evaluation overview explains the role of different evaluation methods. Automated checks help identify issues, but they do not replace human evaluation. Keep keyboard and content review in your acceptance process.
4. Read benchmark context
Our companion FindEverythingAI separates published benchmark versions and preserves agents, task counts, settings and uncertainty. Avoid treating a model’s result through one agent as a promise about another integration. Your local comparison should likewise avoid changing the task or permission set halfway through a run.
A small next step
Choose one familiar bug, freeze its failing example, and compare two approaches on independent working copies. Write a short result: accepted or rejected, important defects, correction time and reasons. Expand the evaluation only when that result tells you what remains uncertain.