Haris Morris

Using AI to spin up new features is awesome for speed, but letting it handle the test suite usually creates a mess of brittle tests. They either pass by mocking out half the application or fail because the agent didn't understand the broader system architecture. Lately I've started treating AI-generated code as untrusted until I manually write or review the tests myself.

What kind of review workflow or safety checks do you use to make sure your test suite doesn't slowly rot away?

I've caught AI coding assistants writing tests a few times that looked completely legit at first glance but were actually asserting nothing useful. One test was just checking if a mock function was called instead of verifying the actual output data. Now I've made it a habit to temporarily undo the code fix just to see if the test actually fails. If it stays green the test is just giving a false sense of security. Do you manually audit your automated test suites or do you mostly trust what the generator outputs?

Server-driven configs save so much time, especially for pushing quick copy tweaks or fixing backend bugs without waiting days for app store review.

But supporting older app versions becomes a massive headache once your data models start changing.

We ended up having to maintain three different API response schemas just because some users never update their apps.

Do you use strict version headers to manage old clients or do you force updates after a certain point?