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 BlackBat AI fits

BlackBat AI 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.

BlackBat AI plugs directly into your deploy pipeline, so runs fire automatically on a deploy — or set a recurring schedule and skip the pipeline step entirely. 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. BlackBat AI 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.

Choosing how runs get triggered

There are three ways to start a run, and most teams combine them:

• From the pipeline. Create an API key under Settings → CI/CD and call the CI endpoint after a deploy. The run returns a status you can poll, so a deploy step can wait on PASSED or stop on FAILED. CI/CD triggering is available on paid plans, and the CI/CD walkthrough has the exact requests.
• On a schedule. A recurring run catches regressions that arrive from outside your pipeline, such as a dependency, a feature flag or a content change.
• On demand. Start one from the dashboard after a risky merge or before a release.

Keeping the loop fast and affordable

Continuous means frequent, so cost and time matter:

• Use diff runs for routine checks. They re-crawl only the pages that changed since your last run, and cost less than a full crawl.
• Do a full run periodically, and before major releases, so the baseline stays honest.
• Send results where people already look. Slack and webhook notifications put a failed run in front of the team without anyone opening the dashboard.

The outcome

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