A data-driven approach to test automation design is best characterized by what?

A data-driven approach to test automation design is best characterized by what?

• A. Using action words to describe the actions to be taken
• B. Scaling to support large numbers of users
• C. Being based on Equivalence Partitioning testing techniques
• D. Separating out test data inputs with a generic script that can read the data

Answer: (D)

The data-driven approach to test automation design is best characterized by the ability to separate out test data inputs from the test scripts. This method allows for the use of generic scripts that can read input data from external sources such as files or databases. The primary advantage of this approach is that it enables testers to run the same set of automated tests with different data sets without the need to modify the script itself. This not only enhances the reusability of test scripts but also makes it easier to maintain them. Additionally, it supports more thorough testing by allowing a variety of data combinations to be tested efficiently. By separating the test data, the automation framework can adapt to changes in requirements or input data without requiring significant changes to the test scripts, promoting a flexible and scalable testing environment. This allows for comprehensive testing across different scenarios and conditions, which is a key characteristic of a data-driven testing approach.

The data-driven approach to test automation design is best characterized by the ability to separate out test data inputs from the test scripts. This method allows for the use of generic scripts that can read input data from external sources such as files or databases. The primary advantage of this approach is that it enables testers to run the same set of automated tests with different data sets without the need to modify the script itself. This not only enhances the reusability of test scripts but also makes it easier to maintain them. Additionally, it supports more thorough testing by allowing a variety of data combinations to be tested efficiently.

By separating the test data, the automation framework can adapt to changes in requirements or input data without requiring significant changes to the test scripts, promoting a flexible and scalable testing environment. This allows for comprehensive testing across different scenarios and conditions, which is a key characteristic of a data-driven testing approach.