Bitbucket Pipelines Integration for Autotest.ing
Automate your testing workflows within Bitbucket. Ensure every commit is verified by an AI agent before it reaches production.
Prerequisites
Set up a secured repository variable to store your API key:
- Navigate to Repository Settings → Pipelines → Repository variables.
- Create
AUTOTEST_API_KEY. - Select the "Secured" (lock) icon to protect the value.
Configuration
Basic Setup (npm script)
This configuration installs the agent and runs it against your codebase.
image: node:20 pipelines: default: - step: name: Build and Test caches: - node script: - npm install - npm run build # Install and run Autotest Agent - npm install -g @autotest/cli - autotest run --key $AUTOTEST_API_KEY --project-id "proj_12345"
Advanced: Deployment Gating
Use Bitbucket's "Deployments" feature to track which environment the agent is testing. This allows you to see the "Test" status in the Bitbucket Deployment dashboard.
pipelines: branches: staging: - step: name: Deploy to Staging script: - ./deploy.sh staging - step: name: AI Verification (Staging) deployment: staging script: - npm install -g @autotest/cli - autotest run --url https://staging.myapp.com --env staging
Artifacts
To view logs or screenshots generated by the agent, define them as artifacts.
artifacts: - autotest-logs/** - screenshots/**
Integration with Jira
Because Bitbucket is part of Atlassian, failing a pipeline here will automatically flag the
associated Jira issue as "Build Failed." Ensure your commits reference Jira keys (e.g.,
PROJ-123: Fix login bug) to enable this traceability.
Combining autotest.ing with Jira integration gives your QA team full visibility — a failed AI test automatically surfaces on the relevant Jira ticket, closing the feedback loop without any manual triage.
Ready to integrate?
Connect your Bitbucket repo and run your first AI regression test in minutes.