Create and Manage Data Source | Zoho QEngine Help

Create and Manage Data Source

1. Overview

Test cases need the ability to manage and utilize various data sets for ensuring thorough and effective test coverage. This can be achieved through data-driven testing (DDT), which allows for systematic testing across a range of inputs and scenarios.  For better understanding of DDT, refer to the Understand Data-Driven Testing page. 

To effectively manage your test data in Data-Driven Testing (DDT), Zoho QEngine streamlines this process by centralizing data source management within your project. You can create multiple data sources, each holding various data sets that can be accessed and utilized by different test cases. This flexibility allows test cases across the project to access and reuse data, optimizing the testing process. Additionally, data sources can be stored in variables, allowing for seamless adaptation of test data across different environments, such as different web browsers, and Android or iOS versions. 

For example, you prepare a data source containing various usernames and passwords and use these data sets inside your test case. With the mask functionality, the usernames and passwords can be hidden and securely stored. During the test execution, Zoho QEngine automatically reads these values from the data source and runs the login test iteratively for each set of credentials. This helps identify any issues with the login functionality efficiently. 

2. Create Data Source

Follow the step below to navigate to data sources:

  1. Navigate to Settings > Data Source. 



You can create a data source in two different ways; either choose to enter the data manually or upload a file.

  1. Manual Entry
  2. Upload file to create a data source

2.1 Manual entry

In this method, you manually enter the data to create a data source.

  1. Click Create Data Source.



  2. Provide a name for the data source created. Additionally, you may enter a description to help understand the data source better, although this step is optional. 



In the Data Set section, a row and a column are created by default. Each column represents a parameter, while each row represents a dataset.

  1. Begin by entering values into the created parameter. Click Add Column to introduce additional parameters and Add Row to create more datasets for testing.



    Click the ellipsis { } icon and select the Edit option to specify the data type for the parameters you want to define. Next, enter a Data Set Name to reference the dataset in the test case’s live preview or results.

    Note:

    • You can only create a maximum of 1000 rows and 50 columns in a data set.

    • The Configure option { } provides flexibility in naming your data sets. You can manually enter your own names or select from a list of parameters that you’ve previously defined.

    • Alternatively, you can utilize the parameters created to generate data set names.

    • When configuring newly updated data sets, users can choose to either modify the existing data set name or retain the original name for the current data set.


    Note: Supported data types include String, Integer, and Boolean. By default, columns are set to the String data type. When saving the data source for the first time, you have the option to change the data type. After saving, the data type field is disabled, and the columns can only contain values of the selected type.


  2. Enable or disable Forecast Failure for each dataset to classify it as a success or failure data set.



    Forecast failure allows you to create an intentional failure in the test case for certain data sets. By default, forecast failure is toggled off. You can toggle it on if needed. When forecast failure is toggled on for data sets, the iteration will pass only if the test case fails.

    For example, consider testing password strength in a login system. If forecast failure is toggled on and the system is given a weak password, the iteration will pass only if the login attempt is rejected. This ensures that the system correctly identifies and handles weak or invalid passwords, maintaining the security standards required.


  3. Once you've created the data sets required, click Save.

Note: 
When configuring newly updated data sets, users can choose to either modify the existing data set name or retain the original name for the current data set.


2.2 Upload file to create a data source

You can also create a data source by uploading an external .csv file containing datasets.

  1. Click Upload File to import the external .csv file.



  2. Give the data source a Name, and select a Data Set Name from the dropdown. You can utilize the parameters you have created. 



  3. Click Save. Once a data source is created, you have the flexibility to add additional data sets, as well as make edits to or delete existing ones.


Note: If a column in the uploaded file is missing a value, a null value will be automatically inserted in the data source template.

3. Update Existing Data Source

After creating your data sources, you can manage them seamlessly. The following are the actions that can be performed related to data sources.
  1. Append Data by Importing Data Set
  2. Manage Data Source

3.1 Append Data by Importing Data Set

You can update the data set in a data source either manually or by importing an external file. Manual editing allows you to directly add or modify individual data sets within the platform. This method is useful for making quick updates to the data sets. Further, to add new datasets manually, simply insert additional rows.


However, for bulk updates on data sets, importing an external dataset file is a more efficient approach. Here's how you can upload an external file to update your data source.

  1. Click Import Data to append more values to the data set.



  2. Upload the relevant file and choose a Data Set Name from the dropdown. You can choose parameters created as the data set name.



Note:
  1.  It is mandatory to have all the existing column names in the uploaded file.
  2. New parameters will be appended as new columns.
  3. Newly added columns will be empty for existing rows in the data source
  1. Click Save.


3.2 Manage Data Source

Zoho QEngine simplifies the management of data sets within your data sources. Here are the actions you can perform on the data stored in your data sources:


3.2.1. Manage Data Sets

You can perform edit, delete, and mask actions on the parameters saved in the columns created. 

i. To change the column name, follow the steps below:

  1. Click on Edit from the drop menu.




  2. Change the name and click Save.




ii. To remove the specified columns and their values, follow these steps:

  1. Click on Delete and confirm the action by clicking on delete.



Similarly, you can delete the values in the data set in bulk. Select the required values to be deleted and click delete.


iii. To mask specific columns and their values, follow these steps:

  1. Click on Mask to hide the values in the selected columns. Similarly, click on Unmask to reveal the values again.
Notes
Note: All the admins, managers, and the tester who created the column have permission to perform the unmask action.

3.2.2 Reference

This gives you the list of test cases that references the data sources.

  1. Click on Reference.



  2. From the list, click on View to see the references inside the respective test case.



  3. Click OK.


3.2.3. Delete Data Source

You can perform the delete action to remove required data sources. 

  1. Click the delete icon beside the data source and confirm your choice by selecting Delete.



Note: If the data set is already used in any of the test cases, remove their references to continue with the delete action.


4. Related Links


5. What's Next?

Next Steps
Previous Steps
Next Steps
You've learned how to create and manage data sources. The next step is to understand how data sources are used in test cases to achieve data-driven testing. 
Previous Steps
Before creating data sources, ensure you have a solid understanding of data-driven testing. Learn more about data-driven testing.