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Change-related testing and deployment verification — every time your codebase changes, Bugzy ensures quality hasn’t regressed. When a PR is opened, a ticket moves to QA, or a deployment lands, Bugzy automatically runs the relevant tests and reports results. This is the verify-changes task, and it’s one of the most common ways teams interact with Bugzy.

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Triggers

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Smart test selection

Bugzy reads the PR title, description, and diff to determine which tests are relevant. If the change touches checkout code, only checkout-related tests run. If Bugzy can’t determine relevance, it runs the full smoke suite. For tickets, Bugzy reads the ticket description and linked PRs to identify the affected area.

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Coverage gap handling

If Bugzy detects that a changed feature has no existing test coverage, it can generate tests on the fly — creating new Playwright scripts for the uncovered area before running them.

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Deployment verification

For deployment triggers (e.g., Vercel preview deploys):
1

Webhook fires

The deployment webhook fires with the preview URL.
2

Run smoke tests

Bugzy runs smoke tests against the preview.
3

Post results

Results are posted as a PR comment and GitHub check run.
4

Team sees status

The PR shows “Bugzy QA / Preview Tests” status.

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Multi-channel result routing

Results are reported through all connected channels:
  • PR comments — GitHub
  • Channel messages — Slack or Teams
  • Dashboard — always available
If a product bug is found, it’s filed in the connected issue tracker.

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Verifying AI-generated code

Bugzy doesn’t care whether a PR was written by a human or an AI agent. When an AI coding agent (Claude Code, GitHub Copilot, Cursor, etc.) opens a PR and a preview is deployed, the same verify-changes flow runs — tests execute against the preview, results are posted as a GitHub check run, and the PR shows pass/fail status before any human reviews. This creates an autonomous development loop:
Teams can configure branch protection rules to require the Bugzy check run to pass before merging, creating an automated QA gate for AI-generated code. See the Bugzy helping Bugzy case study for a real-world example.

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Manual verification checklists

For features that require human verification (visual checks, copy review), Bugzy generates a verification checklist alongside automated test results. The team uses this as a guide for manual sign-off.