In this section:

Modifying Data Structures

Different testing scenarios may require the data to be structured in a variety of ways. The data modeling feature enables you to modify data structures that you can use in all of your testing scenarios.

Duplicating Record Types

You can duplicate a record type and its members/keys, including all their properties (constraints, mask, and generation settings).

1. Choose Duplicate Record Type from the record type ellipsis menu.
2. Specify a name when prompted and click Save.

Deleting Record Types

Choose Delete Record Type from the record type ellipsis menu to delete it.

Adding and Deleting Members

You can add members to record types within a data set.

1. Choose a record type and click the Add Member button (+).
   
   
   
   
2. Specify a name for the new member and choose a type from the drop-down menu. You can add a primitive, a list of primitives, or record type as a member of the selected record type (see Data Modeling for information about types of members).
   
   
   
   
3. Choose a data generation mode for the new member. See Mask and Generation Mode Settings for details.
   
   
   
   
4. Configure the mode settings and click Create.

To delete a member from the model, choose Delete Member from the member's ellipsis menu.

Setting Properties

Properties define the kind of interactions you can perform with your members and keys in your data set. Generation and mask settings and data constraints are kinds of properties that are added when you infer constraints or generate data.   

1. Click on a record type and choose Add New Property from the key's or member's ellipsis menu.  
   
   
   
   
2. Define the property in the field that opens.
    
   
   
   
3. Changes are automatically saved when you click away. 

To remove a property:

1. Open the Remove Property menu.
   
   
   
   
2. Click the trash icon.
   
    

Generating Data

You can generate data for members and keys within your data set. Data is generated according to the Mask and Generation Settings.

1. Click on a record type and choose Mask and Generate Settings from the member's or key's ellipsis menu.
   
   
   
   
2. Choose a mode from the drop-down menu.
    
   
   
   
3. The mode determines how the data is generated. Each mode has different options. See Mask and Generation Mode Settings for details.
4. Configure the mask and settings for the type you selected and click Save.
5. Click Generate Data in the toolbar.
   
    
   
   
6. Specify a name for the generated repository, choose the number of records to generate, and select which data sets to include.
   
   
    
7. Click Generate to add the data generation task to the queue.

A new repository will be created and populated with data based on the mask and generation settings. 

Masking Data

You can generate a masked copy of a data set to hide sensitive information stored in your server. A masked data set contains the same number of data entries as the source, but any members/keys with the Mask and Generate Settings configured will be randomized according to the settings. You can also configure the data constraints settings to ensure that the masked data is correctly generated for your test scenarios.

1. Click on a record type and choose Infer Constraints from the member's or key's ellipsis menu (also see Inferring Data Constraints).
2. Click on the constraints to make any necessary adjustments.
3. Choose Mask and Generate Settings from the member's or key's ellipsis menu.
4. Choose a mode from the drop-down menu. The mode determines how the data is generated. Each mode has different options. See Mask and Generation Mode Settings for details.
5. Configure the settings for the type you selected and click Save.

6. Click Mask Data in the toolbar.

 All data members with mask and generate settings will be replaced in a new masked repository.   

Inferring Data Constraints

The Data Modeling feature can automatically set constraints for generating new data based on the data in the repository. You infer constraints for the selected repository by choosing a repository in the sidebar and click the Infer Constraints button.

You can also infer constraints for individual members and keys by choosing Infer Constraints from the key/member ellipsis menu.

CTP will infer that the data is a Boolean, date, number, or string. 

Editing Data Constraints

If you want to edit the constraints, click on the constraint properties and make changes in the Data Constraint Settings editor. 

You can configure the following settings:

Type

Choose the Boolean type if the key/member should be constrained to true or false.

Choose the Date type if the key/member should be constrained to a date. You can configure the following settings:

Choose the Number type if the key/member should be constrained to a number. You can configure the following settings:

Choose the String type if the key/member should be constrained to a string. You can configure the following settings:

Reference

You can configure the key/member to reference a specific data set/record type and key/member in the repository. Data sets are flagged with a :ds. 

Testing Data Integrity

Data constraints can be configured manually by setting properties, using the Infer Constraints functionality, or through the definition file when creating a virtual asset from traffic in Virtualize. The integrity test functionality enables you to verify that the constraints are valid for the data stored in the repository. 

You can test the integrity of individual members or all members in a data set by choosing Integrity Test from the ellipsis menu. 

You can test the data integrity for the repository by clicking the Integrity Test button in the toolbar.

 If no constraints are set, a warning will appear. If the data does not match the constraints, an error will appear and the member will be flagged. 

You can click on the error message to see details in an overlay.

You can view additional information about the integrity tests in the Test Data Assistant Tasks tab.

Mask and Generation Mode Settings

Modes apply to primitive and list of primitive members. You can specify the mask and generation mode when adding members or change them for existing members. 

None

Choose this mode if you do not want to configure mask and generation settings.

Formula

Choose this mode to specify a formula using foreign key constraints between record type members. Simple math, string, and date operations are supported. You can use aggregate functions in your formulas (e.g., SUM, COUNT, etc.). 

See Consuming the Data in Tools for information on the syntax for referencing other record type members.

About Formula Variables 

• Variables start with "${" (dollar sign and open curly brace) and end with "}" (closed curly brace).
• You can point to a member within the same record type using the ${member} syntax.
• If the variable references the same member for which the mask/generate settings are being applied, the original value will be used in the formula.
• You can point to a member within a specified record type using the ${recordType.member} syntax.
• You can point to a key within a specified data set using the ${dataSet:ds.key} syntax.

Example Formula

SUM(${cart_item.price})*${sales_tax.rate})

From Seed

Choose this mode to generate data using values from an existing repository. This mode requires additional settings to be configured.  

Random

Choose this mode to generate random Booleans, dates, numbers, or strings. Dates, numbers, and strings require additional configuration.

Dates

You can configure the following settings for generating and masking random dates:

Numbers

You can configure the following settings for generating and masking random numbers:

Strings

You can configure the following settings for generating and masking random strings:

Ranged

Choose this mode to generate sequential numbers and dates starting with a specified value and incrementing to a specified end value. The configurations for ranged data are the same for random data, but ranged data also has an increment field for specifying how the data should increment.