Analytics Dashboards (Widgets)

Dashboards allow you to create custom analytics views using company-wide data from the Global Analytics overview. Each dashboard is a customizable grid of widgets designed to track specific metrics or organize analytics by categories such as priority, test type, or milestones.

Dashboards operate at the Company level, aggregating test data across all projects in the selected company.

How to Create a Dashboard

To create a new widget:

1. Navigate to ‘Analytics’ tab from the main workspace dashboard.
2. Select ‘Dashboards’ option from the displayed dropdown list.
3. Click ‘Create’ button to create a new dashboard.

4. Enter dashboard title in the displayed modal.
5. Click ‘Create’ button.

An empty dashboard page will be created and added to your dashboard list. You can now begin adding widgets tailored to your tracking goals.

The user who creates a dashboard is designated as its owner. Since dashboards belong to the company, all members with access to the company can view them.

Note

Only company managers can create, edit, or delete dashboards and widgets. Members with read-only access to the company can open dashboards but cannot change them.

How to Add the Widget

A widget is built based on two parameters: a data source that decides which metric to load, and a visualization that decides how to draw it. The same data source can be rendered in multiple chart styles — for example, success rate by date works as a bar chart, a timeline, or a table.

To add a widget:

1. Open your dashboard page, click the ‘Edit’ button.
2. Click Add Widget button.
3. Choose a Data Source. Each option includes a short description and a list of compatible visualizations.
4. Select your preferred Visualization type
5. Enter a Widget Title (not required).
6. Save the widget. It appears on the grid and loads its data.

Note

You can preview live data with active filters applied before saving the widget.

Widget Visualizations

Each data source supports a subset of different visualizations. The form shows only the ones that fit the data you picked.

• Single value — one headline number, such as total automated tests.
• Bar chart and multi-bar chart — compare values across categories or projects.
• Pie chart, multi-pie chart, and circle (donut) chart — show a breakdown as parts of a whole, such as passed, failed, and skipped.
• Timeline — plot a metric over time to see trends.
• Table — list raw rows, such as per-project or per-priority figures.
• Project stats — a comprehensive card for one project: test counts, automation rate, success rate, and defects by priority.
• Run stats — a summary of run outcomes.
• Partial total — a progress indicator that shows part of a total, such as executed versus planned.
• Milestone views — dedicated layouts for a milestone’s run stats, tests, plans, and requirements.

Setting Default Widget View

You can set or change the Default View for a widget in two ways:

1. During widget creation: Choose the default view within the widget configuration modal.

OR

2. After creation: Open Edit mode on the dashboard page and adjust the view directly on the widget card.

Available Data Sources

Data sources fall into a few groups. Choose a data source based on the key metrics you need to analyze.

Group	Data sources	Answers
Test counts	Automated tests, manual tests, all tests — each also available per project	How many tests exist and how automation is split
Run trends	Success rate by date, automation rate by date, test runs by date	How outcomes and automation change over time
Priority and status	Run results by priority and status, failed runs by priority	Where failures concentrate by priority
Summaries	Project stats, run stats	A full picture of one project or run
Milestones	Milestone completion, milestone run stats, milestone tests, plans, and requirements	Progress against a milestone

Arranging and Resizing Widgets Layouts

Dashboards use a 12-column grid and it allows you to shape the layout directly on the page.

To customize the layout:

1. Click ‘Edit’ on the dashboard page.
2. Move: Click and drag a widget by its header.
3. Resize: Drag the edges or corners of a widget.
4. Click ‘Done’ to save your layout.

A widget can span from one column up to the full width of the grid, so a wide trend chart and a narrow counter can sit on the same row.

After saving the layout, you can quickly switch visualization types directly from the widget header options without entering full edit mode.

Filtering Analytics Data

Filters can be applied at the Dashboard level (affecting all widgets) or at the Widget level (affecting an individual widget).

Note

Dashboard-level filters override individual Widget-level filters.

Available Filter Criteria:

• Date range — restrict run-based metrics to a period.
• Projects — limit analytics to one or more projects in the company.
• Tags and labels — narrow to tests that carry a tag or a label value (e.g., @smoke, Priority: High).
• Environments — focus on a specific test environment (e.g., Staging, Production).
• Jira issues — focus on tests linked to specific issues.
• Suites and folders — limit to part of the test tree.
• Milestones — scope data to a milestone.

Available filter values (tags, labels, environments, suites) are dynamically populated from all accessible projects across your company.

Applying Dashboard-Level Filters

1. Click ‘Filter’ icon in the top-right corner on the Dashboard page.
2. Configure your filter criteria. The selected filters will instantly apply to all widgets on the board.

Applying Single Widget Filters

Option A: During Widget Creation

1. Click ‘Edit’ button -> click ‘New widget’ button.
2. Select your data source and configure specific filters within the creation panel.
3. Add the widget to your board.

Option B: On an Existing Widget (Date Filter)

1. Click ‘Edit’ in the top-right corner of the Dashboard page.
2. Click the ‘Edit’ (pencil) icon on the specific widget card.
3. Adjust the date range filter.
4. Save your changes.

Performance & Caching

To ensure rapid loading times across large datasets, widget results are cached automatically. The cache updates periodically on a set schedule to reflect new test runs. Reopening a dashboard or adjusting filters automatically triggers a fresh data load.