Performance smoke
One query per row
A list page runs a separate query for each item, so response time grows with the data.
Termscritical flow (a path a user takes that costs money or trust if it breaks: sign-up, checkout, invite, export) · staging environment (a copy of production, without customer data, where release candidates are tested) · migration (a scripted change to the database schema that ships with a release and should be reversible)
mediumseverity if found in a releasesources verified 2026-09-21
Why AI-assisted code produces it
Generated code fetches inside loops because it reads naturally. CodeRabbit's sample showed higher issue counts across categories in AI-co-authored pull requests, and performance only shows up under realistic data.
AI-co-authored pull requests carried 10.83 issues each against 6.45 for human-only pull requests, about 1.7 times more, in a sample of 470 open-source GitHub pull requests (320 AI-co-authored, 150 human-only).
Source: CodeRabbit, 2025-12-17 · verified 2026-09-21
How we detect it
Count queries per request on the ten most visited pages with a realistic dataset.
Most associated with
Tools: Cursor, GitHub Copilot, Claude Code, Windsurf, Lovable, Bolt
Checklist items that catch it
Critical flows within agreed budgets under baseline load
tool Run a k6 smoke script over the critical flows at the agreed baseline concurrency against staging.
p95 latency for each flow is within the written budget and the error rate is under 1% of requests in the run.
No N+1 query pattern on critical pages
tool Count database queries per request for the ten most visited pages using the ORM's query log.
Query count per request does not grow with the number of rows displayed.
Dots: Starter, Growth, Scale. The item runs on every release for the plans marked.
More in performance smoke
- medium
- medium
Check your codebase for this pattern
The coverage audit lists which catalogue patterns apply to your critical flows. 5 business days, $1,200, credited to a first month.