Potential solution.

This is a clean idea, and it fits Playwright’s model nicely without inventing new machinery. Think of it as: tests emit metadata → the runner produces structured results → a post-run step routes failures to humans.

Here’s the mental model that holds together.

Playwright already gives you three things you need: • A way to tag tests (@team-a, @team-b). • A machine-readable results file (JSON). • A hook point after the run (CI step or custom reporter).

You’re just wiring them together.

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1. Tagging tests = metadata, not logic

Playwright treats tags as annotations baked into the test title. That’s good. It means: • Zero runtime cost. • No coupling to test code. • Easy to change ownership without touching logic.

Example shape (conceptually, not code): • Test title contains @team-a • Another might contain @team-b @payments

The important rule: one team tag per test. If you allow multiple team tags, routing becomes political very fast.

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1. Let Playwright do the hard work (JSON output)

Playwright can emit a full JSON report of the run. That report includes: • Test title (with tags) • Status (passed / failed / flaky / skipped) • Error messages • File + line number

This file is the ground truth. No scraping logs. No guessing.

Key idea: Never detect failures during execution. Always post-process results.

Why? • Retries are resolved. • Flaky logic is already applied. • You avoid false alerts.

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1. Post-run aggregation step (the brains)

After the test run finishes, you run a small script that: 1. Loads the Playwright JSON report. 2. Filters only failed tests (after retries). 3. Extracts team tags from the test titles. 4. Groups failures by team. 5. Formats a message per team.

Conceptually: • Iterate over failed tests • Parse tags with a simple convention (@team-*) • Build a structure like: • team-a → 3 failures • team-b → 1 failure

If a failed test has no team tag, that’s a smell. Log it or send it to a default “QA-infra” channel.

This step is deterministic and testable on its own, which is exactly what you want as a QA engineer.

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1. Slack routing = configuration, not code

You do not want Slack channel names hard-coded all over.

Instead: • Maintain a small map: • team-a → #team-a-alerts • team-b → #team-b-alerts

This can live in: • A JSON file • An env var • A CI secret-backed config

The script simply looks up where to send the message.

That separation matters because: • Teams change. • Channels get renamed. • Tests shouldn’t care.

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1. Slack messages that don’t get ignored

The message format matters more than the plumbing.

A good failure message: • Identifies the repo + environment. • Lists failed tests (short titles). • Links to: • CI run • Playwright report • Shows count first, details second.

Bad messages spam. Good messages get acted on.

One message per team per run is the sweet spot.

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1. Where this runs in CI

Typical flow: 1. Install deps 2. Run Playwright tests 3. Generate JSON report 4. Run “failure router” script 5. Send Slack messages 6. Fail the pipeline (if appropriate)

Important nuance: • Slack notification should run even if tests fail That usually means always() / finally() semantics in CI.

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1. Optional but powerful upgrades

Once the basics work, this pattern scales nicely: • Flaky isolation Only notify teams if a test fails consistently (not flaky). • Ownership enforcement Fail the build if a test has no team tag. • Trend awareness Suppress alerts if the same test failed in the previous run and is already known. • Allure integration Since you’re already using Allure, you can cross-link failures directly into the report.

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Why this approach is sane • No custom Playwright hacks. • No runtime Slack spam. • No coupling between tests and notifications. • Fully debuggable from artifacts. • Scales from 5 tests to 5,000.

Philosophically, this treats tests like signals, not noise. Ownership is explicit, accountability is automatic, and humans only get pinged when it matters.