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Data Profile

Data Profiles enable data-driven testing. They let you run the same test cases multiple times - once per data set row - each time with a different set of parameter values. Instead of creating separate test cases for each scenario, you define one test case and let the data profile handle the variation.

You can create a Data Profile manually inside an environment, or generate datasets with Case Variations and use that profile on the test case.

Data Profile listData Profile list

Open an environment and click the Data Profile tab to see all data profiles associated with that environment.

Name
The data profile name. Click to open and edit.
Updated By
Who last edited it.
Updated Date
When it was last changed.
Actions
Delete the data profile.

How to create a Data ProfileHow to create a Data Profile

Use this path end to end inside an environment:

1
Open an environment

Go to Environments in the sidebar and open an environment by clicking its name.

2
Open Data Profile

Click the Data Profile tab, then click + Create Data Profile in the top right. The Data Profile modal opens where you can configure the profile.

3
Fill in profile details

Fill in the Name (required) to identify this profile and an optional Description to note what it is for.

4
Add data set rows

Add your data set rows and fill in the parameter values for each row. When you are done click Update to save the profile.

Data Profile fieldsData Profile fields

Profile-level fields in the modal:

Name (required)
A unique identifier for this data profile. Example: dataprofile1
Description (optional)
A short note describing what this profile tests or covers.

Each row in the data set table represents one complete test run. When the test cases run with this profile, QAlity executes them once for every row in the table - each row supplying its own set of parameter values.

S.No
Auto-incremented row number (01, 02...). Assigned automatically as you add rows.
Set Name
A label for this data set row that helps you identify it in reports. Example: set1. Use descriptive names like valid-login or empty-form for clarity.
Expected To Fail
A toggle. Enable this if the test case run for this row is expected to fail. Useful for negative test cases where failure is the correct outcome.
Parameter columns
One column appears for each parameter defined in the environment. Enter the value you want substituted for that parameter during this data set row's run.
Actions
Red trash icon to delete this data set row from the profile.
tip

Enable Expected To Fail for known negative test scenarios - for example testing that an invalid login is rejected. This prevents expected failures from showing as false failures in your test reports.

Managing columns and rowsManaging columns and rows

Add column
Click + next to the last column header to add a new parameter slot to every row.
Remove column
Click × next to a column header to remove that parameter column.
Add row
Click + at the bottom left of the table to add a new data set row.
Delete row
Click the red trash icon in the Actions column to delete an existing row.

How to update a Data ProfileHow to update a Data Profile

1
Open the profile

Click the data profile Name in the list to open it. The modal opens with all existing data loaded.

2
Make your changes

Add or remove rows, update parameter values, or toggle Expected To Fail.

3
Save or discard

Click Update to save your changes, or Close to discard and exit without saving.

Running test cases with a Data ProfileRunning test cases with a Data Profile

Select the Data Profile on the test case in the Test Case Overview side panel (next to Environment). When the case runs with that profile, QAlity executes the steps once per data set row. Each row provides a different combination of parameter values, giving you full coverage across your defined scenarios in a single run. The same idea applies when a suite or plan run uses a selected data profile.

note

Data Profiles are not created by the recorder. Create them manually under an environment, or generate datasets with Case Variations.

Best practicesBest practices

Keep focused
One profile per feature or user flow keeps things manageable and easy to debug.
Set Names
Use descriptive names like valid-login, invalid-email, empty-form so results are easy to read in reports.
Fill every column
Empty substitutions at runtime can cause unexpected test failures.
Expected To Fail
Use only for genuinely negative test cases where failure is the intended outcome.
Environments
Define parameters the profile columns use.
Case Variations
Generate positive, negative, and boundary datasets with AI.