Configuration of a Test Data Finder

XDM’s Test Data Finder allows users to search for application-specific Attributes and retrieve test data that meets their criteria. It helps identify more precise test cases for edge scenarios while also offering a broader range of variations for standard test definitions.

For more information, visit: Test Data Finder and Classification Terms.

Related Object References

The following object references are used within the Test Data Finder:

The following references provide additional details on the components and configurations relevant to the Test Data Finder.

Creating Classification Terms

Classification Terms define the Attributes used to categorize test data. Follow these steps to create them:

Go to Data Subsets in the menu and select Classification Terms. Click + Create to add a new Classification Term.
In the New Classification Term panel, enter the following details:

Field

Value

Name

Employee

Columns

Employee ID
Click Save Changes.
Repeat the process to create another Classification Term:

Field

Value

Name

Department

Columns

Department
Click Save Changes again to finalize.

Creating Classification Term Usage, Attributes and Classification Term Relation

In the left sidebar, click the menu category Data Subsets to expand the data subsets menu and select the menu item Application Models.
Open the Application Model by clicking the button on the left side of the HR Department.
Next, open V1 by clicking the button on the left side from the Versions list at the bottom of the page.
Open the panel Test Data Classification at the bottom of the page.
Then select the tab Classification Term Usage and click the button + Create. Enter the following information:

Field

Value

Classification Term

Department

Name pattern

departments

Columns

dept_no

Script language

Groovy

Format Script

return 'D-' + String.format("%s", data['dept_no'])
Click the button Save Changes.
Use the breadcrumbs to navigate two steps back to the version V1 and go back to the panel Test Data Classification.
Then select the tab Attributes from the panel Test Data Classification and click the button + Create. Enter the following information:

Field

Value

Name

Department

Classification Term

Department

Name pattern

departments

Column expression

dept_name

Enumerate values

active
Click the button Save Changes.

Now we need to repeat the same process for the HR Employee Application Model

In the left sidebar, click the menu category Data Subsets to expand the data subsets menu and select the menu item Application Models.
Open the Application Model by clicking the button on the left side of the HR Employee.
Next, open V1 by clicking the button on the left side from the Versions list at the bottom of the page.
Open the panel Test Data Classification at the bottom of the page.
Then select the tab Classification Term Usage and click the button + Create. Enter the following information:

Field

Value

Classification Term

Employee

Name pattern

employees

Columns

emp_no

Script language

Groovy

Format Script

return 'E-' + String.format('%s', data['emp_no'])
Click the button Save Changes.
Use the breadcrumbs to navigate two steps back to the version V1 and go back to the panel Test Data Classification.
Then select the tab Attributes and click the button + Create. Enter the following information:

Field

Value

Name

Salary

Classification Term

Employee

Name pattern

salaries

Column expression

salary

Enumerate values

inactive
Click the button Save Changes.
Use the breadcrumbs to navigate two steps back to the version V1 and go back to the panel Test Data Classification.
For each of the following Attributes, repeat the creation process as described above with the following values:

Field

Value

Name

Gender

Classification Term

Employee

Name pattern

employees

Column expression

gender

Enumerate values

active

Field

Value

Name

Title

Classification Term

Employee

Name pattern

titles

Column expression

title

Enumerate values

active

Field

Value

Name

Hire Date

Classification Term

Employee

Name pattern

employees

Column expression

hire_date

Enumerate values

inactive

It should now look like this in the attribute’s list:

Then select the tab Classification Term Relation and click the button + Create. Enter the following information:

Field

Value

Name

Employee works in Department

Table

dept_emp

From Application Model

HR Employee

From Term

Employee

From Columns

emp_no

To Application Model

HR Department

To Term

Department

To Columns

dept_no

Column values identical to term values

inactive

Click the button Save Changes.

Now we need to repeat the last step for the Application Model HR Department.

In the left sidebar, click the menu category Data Subsets to expand the data subsets and select Application Models.
Open the Application Model by clicking the button on the left side of the HR Department.
Next, open V1 by clicking the button on the left side from the Versions list at the bottom of the page.
Open the panel Test Data Classification at the bottom of the page.

Then select the tab Classification Term Relation and click the button + Create. Enter the following information:

Field

Value

Name

Employee is head of Department

Table

dept_manager

From Application Model

HR Employee

From Term

Employee

From Columns

emp_no

To Application Model

HR Department

To Term

Department

To Columns

dept_no

Column values identical to term values

active

Click the button Save Changes.

The Application Models are now ready, so we can go to the next step.

Creating the Test Data Indexing Task Template

In the left sidebar, click the menu category Tasks to expand the data subsets menu and select Task Templates.
In the tab Analyze, click the button on the left side of Test Data Indexing Task Template to expand the list. Initially, the list may be empty.
Click the button + Create. A panel titled Create Test Data Indexing Task Template opens. Enter the following information:

Field

Value

Name

Scan production employees
Click the button Create and edit.
Enter a description:

Field

Value

Description

Scans the production tables and builds an index for the specified Classification Terms and its Attributes
Next, select the Environment: HR Production.
In the top panel, select the tab Other Options and select Log level: Trace.
Click the button Save Changes.
Open the Tasks panel at the bottom of the page and click the button + Create. Enter the following information:

Field

Value

Name

Scan employees
Click the button Create.
While in your Task Template, locate the task in the bottom panel under the tab Tasks and click the execute button () on the right.
A dialog window opens. Leave Interrupt execution unchecked and click the button Execute and view. This will schedule the task for immediate execution and switch the main view of Executed tasks.

The execution process should look like this for Scan production employees:

Configure Data Shop for the Test Data Finder

In the left sidebar, click the menu category Tasks to expand the task’s menu and select the menu item Data Shops.
Open the Data Shop by clicking the button on the left side of the Copy employee by id.
Click the button Edit. Then in the top panel, select the tab Test Data Finder and enter the following information:

Field

Value

Environment

HR Production

Classification Term

Employee
Click the button Save Changes.

Use of the Test Data Finder

In the left sidebar, click the menu category Tasks to expand the task’s menu and select the menu item Data Shops.
Locate the desired Data Shop and click Place Order ().

Now, an Employee ID field appears with the Test Data Finder below.

Selecting Employee Attributes

Click + to open the Filter Attributes dialog and choose Employee from Attributes of Classification Term.
Click Next and select the following Attributes:
- Hire Date
- Title
- Gender
- Salary
Click Next, review the summary, and click Apply.

Selecting Department Attributes

Click + again and select Department from Attributes of Classification Term.
Click Next and choose the relation path: Employee works in Department.
Click Next and select Department under Attributes.
Click Next, review the summary (Going over relation Employee works in Department), and click Apply.

Now the Configure attribute filters list should appear as follows:

Configuring Filters

Fill in the filters with the following values:

Attribute

Operator

Value

Hire Date of Employee

GREATER

8/2/2010

Title of Employee

NOT_EQUALS

Engineer

Gender of Employee

EQUALS

F

Salary of Employee

GREATER

80000

Department of Department over Employee works in Department

EQUALS

Quality Management

Click the button Find test data to execute the filter and retrieve matching test data.

Store Data Filter

Click the button Store data filter to save the configured attribute filters for future use.
A panel titled Create Attribute Comparison Filter will open. Enter the following details:

Field

Value

Name

Employees work in Quality Management
Click the button Create to save the filter.

Once saved, this filter can be accessed and applied later to streamline the test data selection process.

Load Data Filter

Click the () button to access previously stored attribute filters.
A list of saved filters will be displayed. Select the desired filter to automatically reload its configuration.

By using the Store Data Filter functionality, users can save time by reusing predefined filter configurations. The Load Data Filter feature allows users to quickly retrieve and apply previously saved filters, ensuring consistency in test data selection.

Viewing Results

A Test data matching the filter list appears with the following data:

Click () next to the Employee title to copy the selected Employee IDs.
Paste the copied data into the Employee ID field at the top.
Click () to process the Data Shop request.

Once the Place Order button is clicked, the Data Shop runs the task in the background using the Employee IDs copied from the filtered list.

Viewing Salary Statistics

Click the Statistics button () in the row "Salary of Employee" to open the statistics window.
The statistics window provides key salary, insights and the decile distribution shows salary ranges across percentiles:
Use this data to refine attribute filters based on salary distributions and improve test data selection.

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