How do you run AI tests on every pull request?
A GitHub or GitLab bot installs into your repo; when a PR opens (or you tag it), it reads the PR context, detects the preview deployment your pipeline built and runs exploratory plus relevant regression tests against it, then posts results back to the PR thread – so issues are caught before merge, without leaving the developer's workflow.
Sub-use-cases
Covers PR bot testing, preview-URL testing, CI/CD pipeline gate (blocking or reporting), post-deploy smoke, scheduled pipeline regression, GitHub App (automatic trigger on PR open or label), GitHub Actions Change Review Action (multi-app PRs, label-based triggers, custom pipelines), CLI (`qatech run`, `qatech tunnel`) for local development and non-standard pipelines and API / webhook trigger from any other CI system.
- 01
What gets tested on a pull request
PR-level testing of the change, preview-environment (Vercel/Netlify/Railway) runs, pipeline quality gates on deploy, and post-deploy production smoke.
- 02
How does AI test a PR before merge?
GitHub Actions and GitLab CI are native; other systems trigger via API or webhook. The bot auto-detects the PR's preview URL, runs the plan, and returns pass/fail with screenshots and video to the thread or pipeline.
- 03
When tests run in the pipeline
On every PR and at each pipeline stage – a smoke plan on merge, full regression on release candidates.
- 04
Who runs PR and CI/CD testing
Dev and platform teams who want quality gates earlier, without leaving their tools.
- 05
How QA.tech helps
Code review catches logic errors, not user-facing regressions. QA.tech moves testing to the moment of the PR, so a broken checkout or login is caught before it reaches main.
- 06
Ways to integrate
QA.tech plugs into your pipeline through several entry points: - **GitHub App** – automatic trigger on PR open or label. - **GitHub Actions Change Review Action** – a standalone action for when the App's auto-trigger isn't flexible enough: multi-app PRs, label-based triggers, custom pipelines. - **CLI** (`qatech run`, `qatech tunnel`) – for local development and non-standard pipelines. - **API / webhook** – trigger from any other CI system.
Companies running pull request & ci/cd testing with QA.tech
Tests every pull request in GitHub and saves 390h of testing per quarter.
Tests every merge request on a preview deploy through self-hosted GitLab.
Wired QA.tech into a custom Azure DevOps pipeline via API.
Runs shift-left PR tests in GitHub.
Uses QA.tech as a PR quality gate.
Triggers the full E2E suite from a GitHub commit message.
FAQ
Common questions
- Does QA.tech test the PR's preview deployment automatically?
- Yes – the bot detects the preview URL and runs against it, no per-PR config.
- Can QA.tech block a deploy on failure?
- Yes – wire it as a blocking check; non-critical tests can report without blocking.
- Which CI systems are supported?
- GitHub and GitLab natively; Bitbucket, Azure DevOps and others via API/webhook.
Related use cases
Automated Regression Testing
Regression testing re-checks that existing features still work after a change. To automate it with AI, you group tests into a regression plan written as plain-language goals, and agents run the whole suite in parallel on every deploy. A 50-test suite that took hours by hand finishes in around ten minutes, and the tests don't need rewriting when the UI shifts.
ReadEnd-to-End Testing
End-to-end testing verifies a complete user journey works from start to finish, the way a real user experiences it. With AI you describe the journey as a goal and an agent carries it out – logging in, navigating, filling forms, verifying the outcome – across your whole app. QA.tech runs these in parallel and re-navigates when the UI changes, so long flows don't collapse on a renamed button.
ReadExploratory Testing
Exploratory testing means investigating an application to discover how it behaves and where it breaks, rather than following a fixed script. AI can do this: an agent clicks through a new or changed feature, finds the flows and edge cases, and proposes test cases for what it sees. With QA.tech this runs automatically when a feature lands.
Read
Notifications & Activity Feed Testing
Next →QA for AI-Generated Code & Agentic SDLC (MCP)
Your code ships daily. Can your testing keep up?
QA.tech agents test your product autonomously, so moving fast never means shipping broken. See it run on your own app in a 30-minute demo.