Custom Widgets in TestMu AI Analytics Dashboards

Custom widgets in TestMu AI Analytics Dashboards allow you to create personalized visualizations of your test data. This feature enables you to gain insights into your testing process and make data-driven decisions.

Prerequisites

Before creating a custom widget, ensure you have:

1. An active LambdaTest account with access to Insights
2. Executed tests on the LambdaTest platform to generate data
3. Access to the Custom Widgets feature

Common Steps for Creating Custom Widgets

The following steps are common to all custom widget types. After completing these steps, proceed to the widget-specific configuration based on your chosen visualization type.

Step 1: Navigate to Insights

1. Log in to your LambdaTest account.
2. Navigate to the Insights section from the left sidebar or visit https://analytics.lambdatest.com/.
3. Click on the Dashboards tab.

Step 2: Create a New Dashboard or Select Existing

1. Click on the + Create New button located at the top right of the dashboard list.
2. From the dropdown menu, select Custom Widgets.

Step 3: Choose a Product

Select the product for which you want to create the widget. Available options include:

• Web Automation: Visualize web testing data
• App Automation: Analyze mobile testing data
• HyperExecute: Monitor HyperExecute test runs
• Real Time Testing: Visualize real-time testing data
• Real Device Testing: Analyze real device testing data
• Smart UI Testing: Visualize Smart UI testing data

Step 4: Select Data Source

Choose the data source that will power your widget:

• Tests: Visualize test execution data including pass/fail rates, test counts, and execution times
• Auto Heal: Visualize Auto Heal statistics and trends (available for Web Automation and HyperExecute only)

Step 5: Configure Filters (Optional)

Use filters to narrow down your data based on specific requirements:

1. Click + Add a Filter to add filter criteria.
2. Select a Key from the dropdown (e.g., browser, OS, resolution, custom data).
3. Choose an Operator (equals, contains, greater than, etc.).
4. Select or enter the Value to filter by.
5. Add multiple filters as needed to refine your dataset.

tip

Use filters to focus on specific release versions, custom parameters, or other attributes. This helps create more targeted and actionable visualizations.

Next Steps: Widget-Specific Configuration

After completing the common steps above, proceed to configure your specific widget type:

• Heatmap Widgets: Create heatmap visualizations to identify patterns across multiple dimensions
• Bar and Line Chart Widgets: Create bar and line charts to visualize trends and comparisons
• Table Widgets: Create structured tables with multiple columns and aggregations

Features of Custom Widgets

Supported Products

• Web Automation: Create custom widgets to visualize web testing data, including test execution times, pass/fail rates, and more.
• App Automation: Visualize mobile testing data, including device usage, OS versions, and app performance metrics.
• HyperExecute: Create custom widgets to monitor your HyperExecute test runs, including execution times, pass/fail rates, and more.
• Real Time Testing: Visualize real-time testing data, including browser and OS usage, test execution times, and more.
• Real Device Testing: Create custom widgets to monitor your real device testing data, including device usage, OS versions, and app performance metrics.
• Smart UI Testing: Visualize your Smart UI testing data, including test execution times, pass/fail rates, and more.

Advanced Filters and Options

• Select Keys: Choose the keys you want to visualize in your custom widget. You can select multiple keys to create a more comprehensive view of your data.
• Choose Operators: Select the operators you want to apply to your data. This allows you to filter and manipulate your data to create the desired visualization.
• Select Values: Choose the values you want to visualize in your custom widget. This allows you to create a more focused view of your data.

Available Visualization Types

Custom widgets support multiple visualization types, each optimized for different use cases:

• Heatmap Widgets: Visualize data density and relationships across multiple dimensions using color intensity and box sizes
• Bar Chart Widgets: Compare values across categories using rectangular bars
• Line Chart Widgets: Track trends and changes over time using connected data points
• Table Widgets: Display structured data with multiple columns, aggregations, and grouping options
• Pie Chart: Visualize the distribution of values across categories (documentation coming soon)
• Billboard: Display key metrics prominently on your dashboard (documentation coming soon)

Available Keys for Custom Widgets

The following keys are available for configuring custom widgets across all visualization types. The availability of specific keys depends on the selected product and data source. These keys can be used for filtering, grouping, axis configuration, and column definitions depending on your widget type.

note

Choose keys that represent categorical dimensions (like browser, OS, project name) for grouping and comparisons, and numeric or countable fields for aggregations. For time-based trends, use timestamp fields.

Web Automation Keys

The following keys are available for Web Automation widgets:

Key	Description	Recommended Use
browser	Browser name (Chrome, Firefox, Edge, Safari, etc.)	Grouping, filtering, X-axis
browser_version	Browser version number	Grouping, filtering
build_name	Build name identifier	Grouping, filtering, row identifier
build_status	Status of the build	Grouping
buildtag_name	Build tag name	Grouping, filtering
create_timestamp	Test creation timestamp	X-axis (time trends)
custom_data.isFlakyTest	Flaky test indicator from custom data	Grouping, filtering
custom_data.product	Product identifier from custom data	Grouping, filtering
device	Device name/type	Grouping, filtering
duration	Test execution duration	Y-axis (with aggregation), column (with aggregation)
end_time	Test end timestamp	X-axis (time trends)
failure_category	Category of test failures	Grouping
os	Operating system (Windows, macOS, Linux)	Grouping, filtering, X-axis
os_version	Operating system version	Grouping, filtering
product	Product name	Grouping, filtering
project_name	Project name	Grouping, filtering, row identifier
resolution	Screen resolution	Grouping, filtering
start_time	Test start timestamp	X-axis (time trends)
status	Test status (passed, failed, etc.)	Grouping (most common)
test_id	Test identifier	Y-axis (with Count aggregation), column (with Count aggregation)
test_name	Test case name	Row identifier, filtering
test_type	Type of test	Grouping, filtering
testtag_name	Test tag name	Grouping, filtering
username	User who executed the test	Grouping, filtering

Custom Data Keys: Any custom data keys you've defined in your test capabilities can also be used.

App Automation Keys

The following keys are available for App Automation widgets:

Key	Description	Recommended Use
app_name	Application name	Row identifier, grouping
app_type	Type of application	Grouping
brand	Device brand name	Grouping, filtering
build_name	Build name identifier	Row identifier, grouping
build_status	Status of the build	Grouping
build_type	Type of build	Grouping
create_timestamp	Test creation timestamp	X-axis (time trends)
custom_data.isFlakyTest	Flaky test indicator from custom data	Grouping, filtering
device	Device name/model	Row identifier, grouping
duration	Test execution duration	Y-axis (with aggregation), column (with aggregation)
end_time	Test end timestamp	X-axis (time trends)
failure_category	Category of test failures	Grouping
os	Mobile OS (Android, iOS)	Grouping, filtering, X-axis
os_version	OS version number	Grouping, filtering
product	Product name	Grouping, filtering
project_name	Project name	Row identifier, grouping
start_time	Test start timestamp	X-axis (time trends)
status	Test status (passed, failed, etc.)	Grouping (most common)
test_id	Test identifier	Y-axis (with Count aggregation), column (with Count aggregation)
test_name	Test case name	Row identifier, filtering
test_type	Type of test	Grouping, filtering
username	User who executed the test	Grouping, filtering

Custom Data Keys: Any custom data keys you've defined in your test capabilities can also be used.

HyperExecute Keys

The following keys are available for HyperExecute widgets:

Key	Description	Recommended Use
app_name	Application name (for app tests)	Row identifier, grouping
app_type	Type of application	Grouping
browser	Browser name	Grouping, filtering
browser_version	Browser version number	Grouping, filtering
build_name	Build name identifier	Row identifier, grouping
build_status	Status of the build	Grouping
buildtag_name	Build tag name	Grouping, filtering
create_timestamp	Test creation timestamp	X-axis (time trends)
custom_data.product	Product identifier from custom data	Grouping, filtering
device	Device name/type	Grouping, filtering
duration	Test execution duration	Y-axis (with aggregation), column (with aggregation)
end_time	Test end timestamp	X-axis (time trends)
failure_category	Category of test failures	Grouping
job_created_at	Job creation timestamp	X-axis (time trends)
job_labels	Job labels	Grouping
job_status	Status of the HyperExecute job	Grouping
job_test_type	Type of test in the job	Grouping
os	Operating system	Grouping, filtering
os_version	Operating system version	Grouping, filtering
product	Product name	Grouping, filtering
project_name	Project name	Row identifier, grouping
resolution	Screen resolution	Grouping, filtering
stage_name	Stage name in the job	Grouping
stage_status	Status of the stage	Grouping
stage_type	Type of stage	Grouping
start_time	Test start timestamp	X-axis (time trends)
status	Test status (passed, failed, etc.)	Grouping (most common)
test_id	Test identifier	Y-axis (with Count aggregation), column (with Count aggregation)
test_name	Test case name	Row identifier, filtering
test_type	Type of test	Grouping, filtering
testtag_name	Test tag name	Grouping, filtering
username	User who executed the test	Grouping, filtering

Custom Data Keys: Any custom data keys you've defined in your test capabilities can also be used.

Use Cases for Custom Widgets

Custom widgets can be used for various purposes, including:

• Tracking Test Execution: Visualize the number of tests executed, passed, and failed over time to monitor your testing progress.
• Analyzing Test Performance: Identify trends in test execution times and pinpoint areas for improvement.
• Monitoring Browser and OS Usage: Gain insights into the most popular browsers and operating systems used in your tests to optimize your testing strategy.
• Comparing Test Results: Compare test results across different environments or configurations to identify discrepancies and ensure consistent performance.
• Visualizing Test Coverage: Create visualizations to understand the coverage of your tests across different features or components of your application.

For detailed widget-specific use cases and examples, see:

• Heatmap Widget Use Cases
• Bar and Line Chart Use Cases
• Table Widget Use Cases

If you have any questions or need assistance with creating custom widgets, please reach out to our support team at [email protected] or visit our Support Center.