7 Advantages of Data Driven Testing You Should Know

 Automation of the development, use, and management of practically infinite pools of test input data are possible through data-driven or parameterized testing. This manual contains all the information you require to integrate data-driven testing into your current or future test automation methods. Data-driven testing has become very popular in the recent past. 

The key benefits of data-driven testing: 

• Reusability: By doing this, the test scripts and test data are logically and clearly separated. For various sets of test input data, you won’t need to modify the test cases repeatedly. Test data and test scripts can both be reused because variables and logic are separated. Changes to one will not impact the other, whether they are made to the test script or the test data. Testing can be done without changing a test case if more test data has to be introduced. The test data won’t be impacted if a programmer decides to alter the code in the test script. 

• Regression testing: An essential component of regression testing is having a collection of test cases that are automatically launched after each build. To prevent the software’s previously effective functionality from being harmed, the latest iteration of the program has undergone these additions. The speed of this procedure is increased through data-driven testing (DDT). Regression tests can be performed for multiple data sets in end-to-end workflow since DDT uses different sets of data values for testing. 

• Testing Positive and Negative Data: Checking the negatives, or exceptions, is just as vital as testing the positives, which is what everyone does. The capacity to manage exceptions is a key performance indicator for a system. These exceptions could result from a worst-case situation replicated in the system at some point. To effectively handle these exceptions, a system must be established. 

• Driving dynamic assertions: It’s crucial to drive dynamic assertions that incorporate the most recent values and circumstances into the pretest ones. The importance of verifications increases during code updates and new releases. Currently, it’s critical to have automated scripts that can supplement these dynamic assertions, i.e., add what has already been tested to the current test lines. 

• Reducing manual work: To start an automated workflow, teams still frequently use manual interventions. It is best to reduce this. Because a manual trigger is never an effective approach to testing a navigational flow, it is best to develop a test script that can accommodate workflows with numerous navigational or re-directional paths. 

• Taking perspectives into consideration: Viewpoints should also be taken into account when analyzing the test cases. This test-taking strategy is more perceptive than logical. Run simple tests to prevent a break or an exception that is predicted at some point in the workflow if you’re interested in checking it. However, the design's existing network will be completely covered by extending the same tests to cover other characteristics like security and performance. 

• Increased clarity: There is clarity in the creation and upkeep of both since the test data and test scripts are stored in distinct folders or places. If test data needs to be changed, it is simple to do so without consulting the testing scripts, and vice versa. 

The process of managing test data includes planning, designing, storing, and retrieving test data. This is known as test data management. Test data management ensures that test data is of the highest caliber, is required in a timely manner, is in the required quantity, and is correctly formatted.