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Evaluation guide

How to evaluate AI code completion

Measure whether inline completion improves delivery instead of merely generating more code.

6 minute read
Short answer

What to do

Evaluate code completion with acceptance rate, correctness after tests, edit distance, interruption cost, and task completion time. Raw suggestion volume is not a useful success metric.

Use normal development work

Include routine implementation, tests, configuration, documentation, and unfamiliar code. A synthetic typing exercise can overstate value because it excludes navigation, reasoning, and review.

Measure accepted value

Record whether a suggestion was accepted and how much it changed before merge. Pair that with test results and defects found during review. High acceptance can still be harmful if developers accept plausible but incorrect code.

  • Accepted suggestions that survive review
  • Time from task start to passing checks
  • Rework caused by generated code
  • Developer-reported distraction

Separate speed from quality

Compare similar tasks over several days and avoid treating one developer's result as universal. Report speed and quality separately, then decide the acceptable tradeoff for each codebase.

Practical checklist

Baseline captured
Representative work sampled
Acceptance tracked
Post-acceptance edits tracked
Tests run
Developer feedback collected

This guide is an editorial framework, not a product endorsement. Recheck vendor documentation and your organization's requirements before making a purchasing or security decision.