Analytics

Visualize test suite trends, track performance metrics, and identify patterns over time

Analytics Section provides a comprehensive view of your test suite health through interactive charts and trend analysis. Monitor key metrics to understand how your tests perform over time and make data-driven decisions about your testing strategy.

• Performance trends — Track duration and success rates across builds

• Stability insights — Identify flaky tests and monitor reliability

• Growth tracking — Observe how your test suite evolves over time

Charts

Explore the avaiable charts:

• Run Status - Distribution of run outcomes over time

• Run Duration - Average duration of fully reported runs

• Run Completion - Distribution of runs by completion status

• Run Size - Maximum specs/tests per run

• Test Results - Distribution of test outcomes

• Test Flakiness - Flaky test distribution over time

• Suite Size - Track test suite composition changes

Examples

Here are some practical ways to use Analytics to answer common questions about your test suite.

Investigating a Spike in Test Failures

When you notice an increase in failed tests:

1. Use Test Results filtered by the affected Git Branch

2. Compare the Success Rate before and after the suspected change

3. Cross-reference with Test Flakiness to distinguish between genuine failures and flaky tests

4. Filter by Git Author to identify if specific commits correlate with the increase

Tracking Test Suite Growth Over a Quarter

For sprint retrospectives or quarterly reviews:

1. Open Suite Size with a 3-month date range

2. Use the Rolling Presence smoothing to see stable growth trends

3. Group by Tag to see which feature areas are getting more test coverage

4. Export to CSV for inclusion in team reports

Comparing Test Performance Across Browsers

To identify browser-specific performance issues:

1. Open Run Duration and filter by Group (e.g., Chromium, Firefox, WebKit)

2. Compare average durations to spot slower browsers

3. Check Test Flakiness grouped by browser to identify stability differences

4. Use this data to prioritize browser-specific optimizations

Customization

All charts can be filtered to focus on specific subsets of your data. Use the Date Range selector to define the time period for aggregation.

Common Filters

Available on all charts:

Filter	Description
Tag	Filter by Playwright tags
Git Author	Filter by commit author
Git Branch	Filter by branch name

Some charts support extra filtering options:

Filter	Available On
Group	Run Size, Test Results, Test Flakiness
Playwright Annotations	Test Results, Test Flakiness
Custom Metrics	Test Results

Learn more about annotations and custom metrics in the Playwright Annotations guide.

Trend Lines

Enable trend lines to visualize patterns and smooth out day-to-day variations in your data.

Option	Description
None	Display raw data points without trend analysis
Linear	Fit a straight line showing the overall direction of your metrics
Moving Average	Smooth fluctuations by averaging values over a rolling window

Trend lines help identify long-term patterns that may be obscured by daily noise, making it easier to spot gradual improvements or regressions in your test suite performance.

Export

Export chart data for further analysis or reporting. Click the export button on any chart to access these options:

Format	Description
CSV	Download raw data as a spreadsheet-compatible file
JSON	Download structured data for programmatic use

Exported data includes all applied filters and the selected date range.