Overview of data fabrication

You can find information about an overview of the data fabrication feature that is available in HCL DevOps Test Hub (Test Hub).

Contents

Advantages offered by the data fabrication feature

The data fabrication feature in Test Hub provides the following advantages:
  • Efficiency, which enables you to generate large volumes of realistic test data rapidly compared to manually creating datasets that can be error prone and time consuming.
  • Data diversity, which enables you to define various data generation rules and distribution patterns so that you can fabricate diverse test data covering a wide range of scenarios.
  • Data privacy, which enables you to fabricate information that is not based on real data not breaching any issues of data privacy or sensitive data.
  • Data re-usability, which enables you to reuse the fabricated data across multiple test cases and scenarios.

To use the data fabrication feature in Test Hub, you must be aware of the terms and concepts that are generally applied in data fabrication or test data generation.

Data definitions

Data definitions are fundamental building blocks that are used to define data structures and generate test data. You can use the data fabrication feature of Test Hub to create Data definitions and to specify the characteristics and attributes of the test data that you need for your testing scenarios. You can then use the Data definitions to generate synthetic test data that closely resemble real-world scenarios.

Data definition catalog

You can access the catalog for a project from Author > Catalog in the Test Hub UI. The Catalog page provides a structured overview of the available built-in generators and custom generators for a selected project. You can use the Search catalog field to quickly find the data generators by its name and also create new custom categories and new generators for a project in a branch.

The Catalog page displays a collection of built-in data generators in the Basic category. In addition to the Basic category of generators, the Catalog page displays all the custom categories and the generators under them, if any, for the project. You can use the basic and custom generators in Data definitions to generate common types of data. Because they are readily available for immediate use, you can quickly select and apply them in the data definitions of your project.

The Catalog tab contains the following types of generators:
  • Basic generators
  • Custom generators

Using the data fabrication feature

You can fabricate test data by using the data fabrication feature in Test Hub.

You can find the flow of the different tasks that you can perform to fabricate test data. See Task flow: Generation of test data.

To begin with you must create a Data definition or an entity that contains the fields for each type of data.
Important: Test assets or resources cannot be created or edited directly in a branch of the project repository. A temporary branch of the branch in the repository called the Edit branch must be created as a container for assets or resources while you work with them. The contents or changes made by using the Edit branch must be committed or published to the branch in the project repository, which merges the changes to the branch in the repository and removes the Edit branch.

You must add at least one generator as a field in the Data definition to generate test data. You can select any generator from the available generators listed in the Catalog tab of a Data definition. The generators in turn contain different options and fields that generate data by using a built-in database in Test Hub. See Basic generators.

After you add the generators to the Data definition, you can save the Data definition. See Saving a Data definition by adding basic generators with their default settings.

You can generate test data by using the default settings of the basic generators that you added to a Data definition. See Generating test data by using default settings of basic generators.

You can modify the default settings of the options and fields in a generator to suit your specific requirements of test data. You can only modify the settings and fields in a generator when it is added to a Data definition. The modified generators are applicable only to the Data definition in which they were modified. You cannot modify any of the built-in generators or save the modified basic generators in the Basic generators category in the Catalog tab. See Saving a Data definition by adding basic generators with their modified settings.

Custom generators are modifications of the built-in generators that you modify any or all the fields in a generator to suit your specific requirements of your test data. The modified generator cannot be saved to the built-in generator and hence, you must save them as custom generators by providing a name to identify the custom generator. See Management of custom generators.

You can create a custom category in the catalog to contain the custom generators that you create. See Management of custom categories.