Test Coverage: How Much Testing Your AI‑Coded App Needs
Automated tests prove that core features still work after each change. In many vibe‑coded and no‑code projects, coverage starts at zero, which is why small updates can cause surprising failures. You do not need to test everything to gain confidence. You need to test the parts where failure costs you the most.
What to cover first
- New user sign‑up to the first home screen
- Returning user sign‑in and a small, successful edit
- Any path that touches money or personal data
If these work every time, you can change the rest more freely.
How much coverage is “enough”
As a rule of thumb, aim to protect the top 20–30% of flows your users rely on daily. For teams that want a number, 60–80% line coverage is a healthy long‑term target, but do not chase a metric for its own sake. Start where failure hurts trust and revenue, then expand.
Keep tests small and close to reality
Prefer tests that click through a screen the way a person would, even if the test runs against a lightweight version of your app. A few realistic checks beat a pile of fragile ones. Cursor, Claude Code, and related tools can help draft tests once you explain which paths matter most.
Make testing a habit, not a project
Add one or two tests for each feature you ship. Over time, you will have a net that catches breaking changes early. Your team will move faster because you spend less time fixing surprises after release.
If you need help choosing what to test first in an AI‑generated app, Spin by fryga can recommend a minimal set that protects key flows without slowing delivery.
A lightweight path to start from zero
Day 1: Write a small test for sign‑up → home. Day 2: Write one for sign‑in → edit → save. Day 3: Protect one money or admin action. Done. You now have three anchors that warn you when core value breaks. Expand from there only as needed.
Common mistakes to avoid
- Testing implementation details instead of user behavior
- Writing brittle tests that fail on harmless UI tweaks
- Waiting for a “testing sprint” instead of adding tests continuously
Aim for durability: a few strong, user‑level tests that survive refactors.
Founder FAQs
Isn’t 100% coverage the goal? No. For AI‑coded apps and no‑code MVPs, the goal is confidence, not a number. Protect the flows tied to sign‑up, sign‑in, payments, and the main job your product does. Let less critical areas mature before adding more tests.
Can AI help write tests? Yes. Cursor, Claude Code, and related tools can draft tests from a written scenario. You still need to verify that the test matches how users behave, but generation speeds up the boilerplate.
What if tests become flaky? Tighten them to focus on outcomes users feel rather than tiny implementation details. If a test still flaps, rewrite or remove it—noise erodes trust.
Case study: three tests, fewer incidents
A small team shipped a no‑code MVP enhanced with AI app generation. Early releases caused frequent breakages in sign‑up, save, and billing. They added three tests covering those exact journeys. Incidents dropped by half, release confidence rose, and the team moved faster because they spent less time firefighting after each deploy.