A team measures throughput and finds developers merge GitHub Copilot suggestions far faster than hand-written code, but their post-release defect rate has climbed over the same period. A lead wants a mitigation that targets the most likely cause rather than simply slowing everyone down. Which approach best addresses the underlying harm?
- ADisable inline completions for the whole team and permit only Copilot Chat, since chat answers are checked more carefully before code lands.
- BMove the organisation from Copilot Business to Copilot Enterprise so the stronger models reduce the number of defective suggestions reaching the branch.
- CRequire a focused human review and test pass on accepted suggestions, since the rising defects point to automation bias rather than raw speed. Correct
- DTurn on the duplication detection filter for the organisation so suggestions matching public code are blocked before they can introduce the defects.
Why A is wrong: This is tempting because chat feels more deliberate, but the surface is not the cause and removing completions blunts a useful tool without addressing the unchecked acceptance behaviour.
Why B is wrong: A plan change is appealing as a quick fix, but no plan validates correctness for the developer, and the defects stem from unreviewed acceptance, not from model tier.
Why C is correct: The pattern of fast merges plus rising defects is the signature of automation bias, where developers trust fluent output without scrutiny, so a review and test gate targets the real cause.
Why D is wrong: Duplication detection sounds protective, but it only addresses verbatim public-code matches and licence risk, not the correctness defects caused by accepting suggestions without review.