A seven-factor rubric for scoring author type, blast radius, test coverage, dependency risk, and policy conflict so each pull request routes to lightweight, standard, or deeper human review.
Studies link AI coding assistants to more bugs, more churn, and lower delivery stability. Here's how to set an AI contribution threshold from your own data, then monitor and enforce it.
AI-generated code ships fast but can compound technical debt silently. GitClear's churn data shows why, and a few metrics and gates let you catch the debt before it turns into incidents.
Engineering teams track PRs merged and lines written. Few track whether AI-generated code survives 90 days in production without incident. Here's what to measure instead.
AI-generated code can pass traditional review and still fail later in production. Its distinct failure modes call for new quality gates and post-merge tracking.
