Continuous Testing

The problem

Continuous delivery means code moves to production constantly. Continuous testing is supposed to be the counterpart — verification that keeps pace. In practice, the "testing" half is usually a scripted suite that (a) only covers what someone wrote and (b) generates its own backlog of maintenance every time the UI changes, so the pipeline is red for reasons that aren't real bugs.

Why the usual approach falls short

• A fixed suite doesn't keep pace. Delivery speeds up; the suite's coverage grows only as fast as someone authors tests.
• Maintenance interrupts the flow. Broken selectors block merges and pull engineers off feature work.
• Flaky tests break trust in the pipeline. Once people rerun until green, the gate is theatre.

How Manta fits

Manta gives you a testing layer that keeps up on its own: it explores the whole app on every run, re-learns it when it changes, and reports regressions — with no suite to maintain and no selector backlog blocking the pipeline. Combine that with plain-English checks for the flows that must always hold, and you have continuous coverage that grows with the product instead of lagging it.

CI/CD integration via API — so runs fire automatically on a deploy — is on the roadmap. Today, teams schedule runs and trigger them on merges to main. The test automation guide covers where continuous runs fit alongside unit and integration tests, and the autonomous testing guide explains the adaptive-coverage model.

What it looks like in practice

1. Unit and integration tests run on every pull request (fast, precise).
2. Manta runs on merges to main and before releases — the broad "does it still work end to end" layer.
3. Plain-English checks guard the business-critical journeys on every run.

The outcome

A verification loop that matches your delivery loop: broad, adaptive, and low-maintenance, with a small precise scripted suite underneath.