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What is the web-platform-tests dashboard?
The web-platform-tests dashboard is a public results browser for the web-platform-tests (WPT) suite, hosted at web-platform-tests dashboard. It shows how different browsers and browser versions perform on the same cross-browser test suite, so you can compare pass/fail results across engines rather than trusting a single vendor's claims.
What it is useful for
- Checking browser support for a feature. Look up a test directory (for example, a CSS or DOM feature area) and see which browsers pass the relevant tests.
- Comparing engines side by side. Results are grouped by browser and version, making it easy to see where implementations diverge.
- Tracking regressions over time. Because runs are recorded per version, you can spot when a browser started failing tests it previously passed.
- Deciding whether to rely on a feature. If most major engines pass the tests for a feature, that is a practical signal it is safe to use; scattered failures suggest you need a fallback.
How to read it sensibly
The dashboard reports test outcomes, not user-facing quality. A failing test may reflect a spec edge case that rarely matters in practice, and a passing suite does not guarantee identical rendering. Treat results as evidence about conformance, then confirm behavior in the browsers your audience actually uses.
A concrete next step
If you are evaluating a feature, find its test directory on the dashboard, filter to the current stable versions of the browsers you support, and check whether failures cluster in one engine. If they do, plan a feature detection or polyfill path; if results are broadly green, proceed with normal progressive enhancement.
How can I check whether a specific browser supports a web platform feature?
Use the web-platform-tests dashboard to see pass/fail results for a feature across browsers, then confirm in the browser you actually target before relying on it.
How to check
- Identify the feature precisely. Note the API or CSS property name and any subfeatures (for example, a method on an interface versus the interface itself). Results are per test, so a single "supported" label can hide partial implementations.
- Search the dashboard for the feature or its test path. The dashboard groups results by browser and version, so you can compare stable and experimental channels side by side.
- Read the result as a ratio, not a yes/no. A feature passing most tests but failing a few edge cases will behave inconsistently in real pages. Check which specific tests fail.
- Verify locally. Run a small snippet in the target browser and check for the API's presence and basic behavior. Feature detection in code (checking for the method or property before calling it) is more reliable than version-based assumptions.
What the results tell you
| Signal | What it suggests | Practical use |
|---|---|---|
| Broad passes across browsers | Mature, safe to use with a fallback | Ship directly; keep a guard for older versions |
| Passes in some browsers only | Partial support | Feature-detect and provide a polyfill or alternate path |
| Mostly failing, some passing | Experimental or in progress | Treat as progressive enhancement, not a dependency |
| Mixed results within one browser | Inconsistent implementation | Test the exact cases your users will hit |
A concrete scenario
If you plan to use a newer JavaScript API in a form widget, look up that API on the dashboard, check the browsers your analytics show as most common, and note any failing tests. If the failures affect your use case, write the code to check for the API first and fall back to a simpler input. That way the page works everywhere and improves where support exists.
Decision criterion
Use the dashboard to decide whether a feature is broadly safe; use a quick local test to decide whether it works for your specific code path. When the two disagree, trust your local test for that browser version.
How do I compare test results across different browsers and browser versions?
Use web-platform-tests dashboard to compare pass/fail results for the same web-platform tests across browsers and browser versions, then read the differences by feature area rather than as a single overall score.
A practical comparison workflow
- Pick the tests that matter to you. Filter to the feature or test directory you are shipping against (for example, a specific API or layout behavior) instead of scanning the whole suite.
- Line up browsers side by side. The dashboard's multi-browser views let you see the same test rows against each engine, so a red/green mismatch points to a concrete divergence.
- Compare versions within one browser. Switching between stable and preview channels shows whether a failure is a known gap being fixed or a regression.
- Check the test's own history. A result that flips between runs often signals flakiness; treat those rows as uncertain rather than as a browser defect.
What the differences usually mean
| Pattern | Likely reading | Useful next step |
|---|---|---|
| Fails in one engine, passes elsewhere | Genuine interoperability gap | Check the spec text and file a bug with the test link |
| Passes in preview, fails in stable | Fix in progress | Plan a fallback until the stable release |
| Inconsistent across runs | Flaky or environment-dependent test | Re-run before drawing conclusions |
| Fails everywhere | Possibly a test or harness problem | Inspect the test source before blaming browsers |
Choosing where to start
If you maintain a library, focus on tests covering the APIs you call and compare the two engines your users rely on most. If you are evaluating whether to adopt a feature, compare stable channels only, since preview results describe future behavior rather than what visitors see today. For spec-level context alongside results, the test sources and discussions at GitHub and the standards text at MDN Web Docs help explain why a divergence exists.
How can I tell if a test failure is a known issue rather than a real regression?
Check whether the test has a linked bug or an existing failure history on the dashboard before treating a red result as a regression. On web-platform-tests dashboard, a failure that already appears in earlier runs for the same browser and test is a known issue; a failure that appears only in the new run is a candidate regression.
Practical checks
- Compare against a baseline run. Open the same test in an older run for the same browser and platform. If it failed there too, it is pre-existing rather than caused by your change.
- Look for an issue reference. Known failures are often tracked in the test's metadata or an associated bug. A linked issue is strong evidence, but confirm the failure mode matches; a new error message under an old bug can still be a regression.
- Check the pattern across browsers. A failure isolated to one engine and one run is more suspicious than one that appears consistently across many runs.
- Check flakiness. If the test alternates between pass and fail across nearby runs, treat it as unstable rather than a clean regression until it fails consistently.
A concrete scenario
You change a layout feature, and a CSS test turns red in Chrome. You open the test's history and see it failed in the previous ten runs as well. That points to a known issue, so your change is unlikely to be the cause. If instead the test passed in every prior run and fails only after your change, investigate it as a regression, even if an old bug happens to be linked.
Decision rule
Treat a failure as a known issue only when both conditions hold: it appears in a baseline run, and its failure mode matches the tracked issue. If either condition fails, investigate as a regression. When in doubt, reproduce the test locally against both the old and new builds; that single comparison resolves most ambiguity.
How do I find which web platform tests apply to a particular feature I'm working on?
Use the dashboard's search and filtering to narrow the full web-platform-tests (WPT) suite down to the tests that exercise the feature you're working on, then read the individual test files to confirm what they actually assert. The web-platform-tests dashboard is the main place to start: it indexes WPT results across browsers, so it doubles as a map of which tests exist and how engines currently fare on them.
A practical workflow
- Search by the feature's API or spec term. Use the spec's interface name, CSS property, or element name (for example a specific method or attribute) rather than a vague description. WPT test paths and titles usually echo the spec vocabulary, so matching that vocabulary is the fastest route.
- Filter by test area. WPT is organized into directories by area (DOM, CSS, HTML, fetch, and so on). Browsing the relevant directory catches tests whose titles don't contain your exact search term.
- Check current results. The dashboard shows pass/fail across browser engines. A cluster of failures in one engine often points to a specific behavior worth reading closely; a block of passes tells you the baseline behavior other engines already agree on.
- Open the test source. The dashboard links to the test files. Read the assertions to see the precise expected behavior, since a test's title can be broader than what it checks.
Deciding which tests matter
| Signal | What it suggests |
|---|---|
| Your feature name appears in the test title | Directly targeted; start here. |
| Test lives in the feature's spec directory | Likely relevant even if the title is generic. |
| Consistent failures across engines | Behavior may be underspecified or newly added; verify against the spec. |
| Passes everywhere | Establishes baseline behavior your change must not break. |
Next step
Once you have a shortlist, run those specific tests locally against your build rather than the whole suite. WPT supports running individual files, which keeps the feedback loop short while you iterate. If the dashboard shows no tests for your feature, that itself is useful: it may mean the feature is new and tests are still being written, so check the relevant spec repository for in-progress coverage.
Can I use the dashboard results to decide when a feature is safe to ship to users?
Yes, but as evidence to inform a shipping decision, not as the decision itself. web-platform-tests dashboard aggregates results from the shared web-platform-tests suite across browsers and engines, so it shows how consistently a feature behaves in automated tests. That is strong signal for standards conformance and cross-browser interoperability. It does not tell you whether the feature is safe for your users in your product.
What the dashboard is good for
- Interop gaps: Seeing whether other engines pass the same tests helps you judge whether your behavior will match what developers and sites expect.
- Regression tracking: Comparing runs over time shows whether a change broke passing tests, which is useful before a release.
- Spec alignment: Failing tests often point to ambiguous spec text or a genuine implementation bug worth fixing first.
What it cannot tell you
- Real-world breakage: Tests do not cover every site pattern, legacy quirk or third-party script interaction.
- Performance and UX cost: Passing tests say nothing about speed, memory or confusing behavior.
- Your audience: A feature may be interoperable yet irrelevant or risky for the browsers your users actually run.
- Rollout mechanics: Flags, staged rollout and fallback behavior are product decisions the dashboard does not model.
A practical decision rule
Treat a clean, cross-engine pass as one gate among several. Ship when the dashboard shows the relevant tests passing in the engines you target, your own integration tests cover the feature in realistic pages, and you have a rollout plan (flag or staged percentage) with monitoring for breakage. If results are mixed, check whether the failures are spec ambiguity, a known bug, or a genuine interop risk before deciding.
For concrete next steps, compare your target engines' results for the specific test files covering the feature, then pair that with your own compatibility data—for example Can I use for support context—and your telemetry on which browsers your users actually use.
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