Why AI-Generated DB Migrations Need a Clone-Based Dry Run, Not Just Tests

A commentary published on DEV Community argues that passing test suites are insufficient to validate AI-generated database migrations, since tests only run against the post-migration schema and cannot detect data loss that occurs during execution. The author notes that as AI tools lower the cost of generating migrations to near zero, the volume of unreviewed migrations rises, increasing the risk of silent data destruction in production. Down migrations, which serve as the primary rollback safety net, are often treated as afterthoughts despite being critical when deployments fail. The recommended approach involves cloning the production schema with a representative data sample and running both the up and down migrations while capturing row counts, null rates, and constraint counts at each step. The article, disclosed as part of MonkeyCode product outreach, includes a Bash script implementing this dry-run workflow for PostgreSQL databases.