Data-driven or parameterized testing allows for the automation of the creation, use, and management of almost endless pools of test input data. This document provides everything you need to know to incorporate data-driven testing into your current or future test automation approaches. Data-driven testing has recently gained popularity.

The following are the primary advantages of data-driven testing:

1. Reusability: The test scripts and test data are logically and clearly separated in this manner. You won't have to change the test cases for different types of test input data. Because variables and logic are separated, both test data and test scripts can be reused. Changes to one will have no effect on the other, regardless of whether they are made to the test script or the test data. If more test data must be introduced, testing can be performed without modifying a test case. If a programmer changes the code in the test script, the test data will not be affected.

2. Regression Testing: Having a library of test cases that are automatically triggered after each build is a crucial component of regression testing. The latest generation of the software has undergone these improvements to protect the software's previously effective functionality. Data-driven testing (DDT) increases the pace of this approach. Because DDT employs several sets of data values for testing, regression tests can be run for multiple data sets end-to-end.

3. Testing Positive and Negative Data: While verifying the negatives, or exceptions, is just as important as testing the positives, which is what everyone does, it is also more difficult. A system's ability to manage exceptions is a critical performance indicator. These exceptions may have resulted from a worst-case scenario that was duplicated in the system at some point. A mechanism must be built in order to appropriately handle these exceptions.

4. Drive Dynamic Assertions: It is critical to drive dynamic assertions that incorporate the most recent values and circumstances into the pretest ones. Verifications become more important during code modifications and new releases. Currently, having automated scripts that may supplement these dynamic assertions, i.e., add what has already been tested to the current test lines, is crucial.

5. Reducing manual work: Teams commonly employ manual interventions to begin an automated procedure. It is preferable to reduce this. Because using a manual trigger to test a navigational flow is never an effective method, it is important to create a test script that can accommodate workflows with multiple navigational or re-directional paths.

6. Considering viewpoints: When analyzing test cases, perspectives should be considered as well. This test-taking method is intuitive rather than logical. If you're curious, run simple tests to prevent a break or an exception that is predicted at some point in the workflow. However, by extending the same tests to encompass other qualities such as security and performance, the existing network in the design will be fully covered.

7. Increased clarity: Because the test data and scripts are housed in distinct files or places, their development and maintenance are more clear. It is simple to change test data without consulting the testing scripts, and vice versa.

   
   

   Planning, developing, storing, and retrieving test data are all part of the test data management process. You may utilize a verified Google Sheets connector to effortlessly group all of your test data in one place and automate the entire process. This is referred to as test data management. Test data management ensures that test data is of the greatest quality, that it is required on time, that it is in the required quantity, and that it is structured correctly.