Quote from Mike Ross on April 21, 2026, 11:35 am

I’ve been looking into API testing setups across a few projects, and I keep noticing the same pattern:

Teams say they have “good coverage,” but things still break once they hit production.

Common setup I see:

• Functional tests for key endpoints
• Some Postman collections or scripts
• A bit of automation in CI/CD
• Most testing happens near release

But despite all that, issues still slip through—especially around edge cases, integrations, or real-world data.

So I started digging into what might be missing.

A few things that stood out:

• We test too late instead of during development (no shift-left)
• Heavy reliance on mocks instead of real API traffic
• Static test cases that don’t evolve with the API
• Not enough focus on edge cases (timeouts, invalid inputs, etc.)

I came across this breakdown on
👉 https://keploy.io/blog/community/api-testing-strategies

It suggests a more “continuous + real-data-driven” approach to API testing rather than traditional test-case-heavy workflows.

Also saw tools like Keploy that generate test cases from actual API calls instead of writing everything manually—which seems interesting for scaling.

Curious how others are handling this:

• Are you relying more on mocks or real traffic?
• How do you keep tests updated as APIs change?
• Do you run API tests on every commit or just before release?

Would love to hear what’s actually working in real projects.