Test intelligence platform for modern teams

TestDino is a Playwright test reporting and analytics platform that helps QA and development teams make sense of every test run. It becomes your single source of truth across branches, environments, and pull requests.

By combining AI insights, environmental mapping, branch health metrics, and detailed artifacts such as screenshots and logs, TestDino helps teams move beyond simple test outcomes to understand what’s truly happening beneath the surface.

It answers the real questions that teams care about:

At its heart, TestDino converts raw Playwright test data into actionable intelligence. Each failure is automatically analysed, AI-classified, and paired with contextual run evidence screenshots, console output, and trace viewer logs.

When you open a failed test case, you’ll instantly see:

Every member of your team sees test data differently. TestDino respects that with role-based dashboards that speak each language: QA, Developer, and Manager.

Modern Playwright teams often test across development, staging, and production. Subtle differences can create misleading results. TestDino surfaces:

Developers get their own focused view centered on branches, pull requests, and branch health.

Here’s what awaits you:

The Pull Requests view brings testing insights directly into the code review workflow. Instead of switching between CI logs and GitHub, teams can see every PR’s testing health, evidence and risk indicators in one unified view.

Key benefits include:

The Summary tab is built for instant understanding. It automatically groups failed, flaky, and skipped tests by their root cause, allowing teams to diagnose issues within minutes.

Failed Tests: A test is marked Failed when execution completes but ends with an unmet condition or error. Common causes include:

Flaky Tests: When a test behaves inconsistently across runs without any code change, it’s flagged as Flaky. These often pass on retry. Causes include:

Skipped Tests: Tests that don’t run at all are listed as Skipped. Reasons may include:

By opening an individual test run, you can access the Detailed Analysis View for fast triage:

The History Tab lets teams visualize how stability evolves.

The Specs tab provides a file-centric view of a run. This is the quickest way to assess a feature area without leaving the run, allowing you to review results by file (e.g., cart.spec.js).

The left panel lists every spec file with its total tests, counts for pass/fail/flaky/skipped, and total duration. You can sort and filter the list to spot slow files and failing areas fast.

The right panel then lists all tests inside the selected file, including status tags and retry badges.

The Configuration tab provides the complete execution context needed to reproduce a run and detect config drift. It meticulously details:

The AI Insights Tab elevates test analytics by categorizing failures and revealing patterns that might otherwise stay hidden.

KPI Tiles display top-level summaries:

The Specs Explorer table makes it easy to analyze specs with sortable columns:

By clicking on an individual test case from the Test Run Overview tab, you can see all the details that matter about that specific test in the current run:

A performance summary showing:

Each outcome (Passed, Failed, Flaky) appears as a tile showing:

A chronological record of every execution, with key details:

Evidence Panels are available when you open an individual test case from the Test Run Overview tab.

Each test attempt has its own set of detailed panels:

For every failed or flaky test, AI Insights in TestDino explains why it happened and what to do next.

Here’s what’s included:

The Specs Explorer table makes it easy to analyze specs with sortable columns:

Every failure is automatically labeled with AI confidence scores under one of four categories:

AI doesn’t stop at labeling; it explains why a test failed. Each failed test includes:

TestDino groups related errors, timeouts, network failures, or missing elements under Error Variants.

You can filter tests by variant or AI label to see where similar problems cluster.

It flags new failures, regressions, and consistent blockers, allowing teams to detect trends early and track fixes over time.

TestDino’s Analytics module transforms raw Playwright test execution data into clear, visual trends that are actually meaningful.

Instead of sifting through endless logs, QA teams, developers, and engineering leads can finally see where failures concentrate, what’s slowing them down, and how quality evolves.

Analytics gives you four distinct lenses: Summary, Test Run, Test Case, and Environment, each designed to answer specific questions about your Playwright test health.

The Summary view is your project’s heartbeat. It surfaces the key metrics that show how your Playwright tests are performing over time:

This shows daily test runs, split by Passed tests (green) and Failed tests (red). Hover over any date to see exact counts. Key metrics include:

Displays the percentage of executions that gave inconsistent results for the same code. High flakiness signals instability or poor test isolation.

Highlights tests failing for the first time within the selected time window. This helps spot regressions immediately as they appear.

Tracks how often retries occur. A rising retry curve often indicates hidden instability in the suite that retries are masking rather than resolving.

In short, the Summary tab tells you whether your overall Playwright pipeline is healthy, stable, or trending toward trouble.

This lens focuses on pipeline performance and how quickly and efficiently your Playwright test runs complete.

You can track metrics such as:

These metrics are paired with interactive visualizations:

The Test Case view is where you can zoom in on individual Playwright tests to understand their behavior over time.

It includes four major metrics:

Beyond raw numbers, the visual elements bring the data to life:

Modern Playwright teams often run the same tests across development, staging, and production environments, but subtle configuration differences can create misleading results.

The Environment view removes that guesswork.

Here’s what it shows:

The Test Case Management tab in TestDino is a dedicated workspace where teams can create, organize, and maintain all manual and automated test cases within a project. It provides the foundation for structuring test coverage, grouping cases under suites and subsuites, and tracking classifications, automation status, and metadata.

The layout is optimized for clarity: a sidebar shows suite hierarchies, a top bar provides key actions, and the main panel displays test cases in grid or list format.

Organize test cases in nested suites and subsuites to reflect modules, features, or components. This structure makes it easy to manage large repositories.

Switch between List View (high-density table for bulk actions) and Grid View (visual cards for quick scanning). Both allow inline editing and filtering.

Import or export test cases via CSV with mapping, enum alignment, duplicate handling, and validation.

Update multiple test cases at once—move, tag, reclassify, or modify statuses in batch.

Quickly locate test cases using the search bar or multi-filters for status, automation, priority, type, or tags. Multiple filters can be combined for precise queries.

Track the automation status of each case (Manual, Automated, To Be Automated) and mark cases as Flaky or Muted for stability review.

At the top of the workspace, KPI tiles provide a quick snapshot:

List View: A table layout showing Key, Title, Priority, Type, Tags, Status, Automation, and Severity. Checkboxes and action menus support individual or bulk operations.

Suites structure test cases into logical groups. They can represent features, modules, or components, and support nested subsuites for deeper hierarchies.

A test case defines a single validation in your system. It includes:

1. New Test Case: Full creation form with all fields.

Suite Assignment: Primary method for structured organization

Tags: Secondary method for cross-suite grouping (e.g., Smoke, Regression, P1). Use tags to track coverage or run types across suites

When to use what:

Select multiple test cases or suites to perform actions like:

Import CSV: Guided upload with column mapping, enum alignment, duplicate handling, and preview. Unassigned cases are automatically placed in the "Unassigned" suite.

Export CSV: Download selected or filtered test cases, entire suites, or the full project. Exported files match the visible data structure and can be reused for re-imports.

Scenarios:

In modern QA pipelines, testing isn’t just about running checks; it’s about keeping every stakeholder in sync. TestDino’s integrations connect directly with the tools your team already uses, so insights flow where decisions happen.

GitHub Integration:

Playwright runs live in CI, and TestDino works right alongside it. Once connected, TestDino automatically detects test runs from GitHub Actions and posts AI-generated summaries directly to commits and pull requests.

These summaries include:

From any failed or flaky test, you can raise a Jira issue directly in TestDino.

The issue is automatically filled with:

For teams using Linear, TestDino’s integration makes issue creation just as seamless. From a flaky or failed test, you can instantly create a Linear bug report prefilled with:

For teams managing QA tasks in Asana, TestDino automatically fills task fields such as:

TestDino’s Slack App and Slack Webhook bring run insights right into your channels.

Each Slack summary includes:

In 2025, test automation isn’t about running tests, but understanding them.

Pass or fail results don’t tell you whether a failure is a real bug, a UI change, or a flaky test. With AI insights, evidence-first triage, PR-aware workflows, and role-specific dashboards, TestDino transforms Playwright testing from noise into clarity.

Whether you’re a QA engineer fighting instability, a developer ensuring stable merges, or a manager tracking release health, TestDino turns Playwright chaos into confidence.

Try TestDino today and experience how AI-powered test intelligence can bring clarity to every Playwright run.