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Documentation Index

Fetch the complete documentation index at: https://docs.testdino.com/llms.txt

Use this file to discover all available pages before exploring further.

Definitions of the core terms used across TestDino and Playwright test reporting. Each entry gives a one-line definition, the key details, and a link to the feature that uses it.

Quick Reference

TermOne-line definition
Test runOne execution of a test suite that reports results to TestDino
Test caseA single test() or it() block
Test suiteA set of test cases grouped in a spec file or project
Flaky testA test that passes and fails without code changes
Failure classificationThe category assigned to a failure’s root cause
Error groupingClustering failures by shared error message
Trace viewerStep-by-step replay of a test execution
Real-time streamingLive upload of results while tests run
Test executionOne billed unit: a single test case attempt
Code coveragePercentage of source code exercised by tests
Stability scoreA test’s pass rate across its run history
MCPProtocol that lets AI agents query TestDino
Quality gateRules that pass or fail a CI check
Environment mappingRules that tag runs by environment from the branch
Personal Access TokenA user-scoped credential for MCP and the API

Test run

One execution of a Playwright test suite that reports its results to TestDino.
  • Carries metadata: branch, commit, CI provider, duration, pass/fail counts
  • Links to the pull request that triggered it
  • Tracked over time so you can compare runs across branches and pipelines
Explore the run view in Test Runs.

Test case

A single test() or it() block that runs and reports one result.
  • Result is one of: pass, fail, or flaky
  • The smallest unit TestDino tracks
  • The unit used to measure usage
See how cases are tracked in Test Cases.

Test suite

A set of test cases grouped together, usually by spec file or Playwright project.
  • Aggregates suite-level metrics: pass rate, duration, flakiness
  • Helps locate the least stable areas of a codebase
Learn how suites fit together in Key Concepts.

Flaky test

A test that produces different results, passing in one run and failing in another, with no code change. How TestDino detects it:
ScopeSignal
Within a single runFails, then passes on retry
Across runsInconsistent outcomes over time
Sub-categorized by root cause: Timing, Environment, Network, Assertion, or Other. Read how detection works in Flaky Test Detection.

Failure classification

The category TestDino assigns to a failed test based on its likely root cause. The four categories:
CategoryMeaning
Actual BugA real defect in the application
UI ChangeAn expected change that broke an assertion
Unstable Test (Flaky)Non-deterministic behavior, not a real failure
MiscellaneousEverything else (config, environment, infra)
Turns a wall of red into a triage queue, so teams fix real bugs first. See how categories are assigned in AI Failure Analysis.

Error grouping

Clustering of failed and flaky tests by their shared error message within a run.
  • Review 5 underlying errors instead of 40 individual failures
  • Each group shows the affected test count
  • Each group links to every test case it covers
Learn how grouping works in Error Grouping.

Trace viewer

The step-by-step replay of a single test execution. Shows, in sequence:
  • Each action and assertion
  • Network calls and console logs
  • DOM snapshots and screenshots
TestDino renders the standard Playwright trace in-platform, so you debug without downloading artifacts. See it in action in the Trace Viewer guide.

Real-time streaming

Upload of test results to TestDino while the run is still in progress.
  • Package: @testdino/playwright
  • Command: tdpw test
  • Status: experimental, may drop data
For reliable uploads, run tests normally and use tdpw upload after the run. Set it up in Real-Time Test Streaming.

Test execution

The billed unit of usage in TestDino: one attempt of one test case.
Counts as an executionDoes not count
Each test case attemptSkipped tests
Each retry (2 retries = 3 executions)Attachments (screenshots, videos, traces)
See how usage is metered in Billing & Pricing.

Code coverage

The percentage of application source code exercised by a test run, measured with Istanbul.
  • Ingested alongside test results
  • Shows which code paths your Playwright suite reaches
Set it up in Code Coverage.

Stability score

A test case’s pass rate across its run history.
  • Formula: (Passed / Total Runs) x 100
  • Quantifies how trustworthy a test is over time
  • Scoped to the active branch
See per-test trends in Test Case History.

MCP (Model Context Protocol)

The open protocol that lets AI agents read TestDino data in natural language.
  • Works with agents like Claude and Cursor
  • An agent can query test runs, failures, and flaky tests, then reason about them in chat
  • Optional: TestDino works fully through the dashboard, CLI, and integrations
Get started in the MCP Overview.

Quality gate

Rules that determine whether a TestDino CI check passes or fails on a pull request. A gate can block a merge on:
  • Failed tests
  • Flaky tests
  • A drop in pass rate
Configure gates in GitHub Status Checks.

Environment mapping

Rules that tag each test run with an environment based on its branch name.
  • Example: main maps to production, staging maps to staging
  • Lets you filter analytics by environment
  • Lets you compare stability across deployment targets
Set up rules in Environment Mapping.

Personal Access Token (PAT)

A user-scoped credential used to authenticate the MCP integration.
TokenPrefixScopeUsed by
Personal Access Tokentpu_UserMCP integration
Project Access Tokentdp_ProjectPublic API, CLI
Create one in Generate API Keys.

Key Concepts

How test cases, suites, and classification fields fit together

Getting Started

Set up TestDino and upload your first run

Flaky Test Detection

How TestDino finds and categorizes flaky tests

MCP Overview

Connect AI agents to your test data