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What is data driven testing and why is it even important?

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What is data driven testing?

Data driven testing is a specific form of software testing in which an automated test script performs the same steps that a user would in order to provide consistency and quality when applied to multiple use cases. This can be achieved through feature flagging, automation frameworks such as Selenium & Cucumber, and continuous integration when using CI servers such as Jenkins Servers or CircleCI.

This type of testing allows for better code coverage than ever before. Rather than testing manually by going through every possible line of code, tests are able to continuously run on an end-to-end basis instead and at a much higher speed level compared to manual tests.

One of the most important things a software engineer or tester can do is to design and implement tests. A test is a series of instructions that help reduce the risk of errors and provide confidence in the reliability, validity, and security of a program.

Data driven testing has been proven to be more accurate than manual testing by using automated tests that mimic human behaviour during an interaction with a product without any need for programming knowledge. This type of automated testing thus increases speed and reduces costs while increasing quality assurance in every project.

Because it is data-driven, data-driven testing allows for a completely different way of thinking when it comes to developing or fixing software.

In traditional manual testing, the tester has to think about what test cases they will run and how to test a program’s functionality. In contrast, a data driven test does not require a programmer in order for tests to run.

Data driven testing allows for automated tests that mimic human behaviour during an interaction with a product, which takes time consumption out of the equation when it comes to writing new tests or making changes to existing ones.

In order to make testing more effective, software testers use data driven testing. This type of testing approach uses data collected from real-life users in order to develop features or bug fixes in programs automatically.

Pros of data-driven testing

  1. Multiple variables can be tested in a single run
  2. Automation can be done without writing any code
  3. Tests are performed in a production environment
  4. Features and bug fixes can be written in a shorter period of time
  5. The test script runs faster than any human could manually test a program due to the script executing the same instructions over and over again
  6. It is scalable because the test will run faster & more accurately when executed against a larger set of data

Cons of data-driven testing

  1. The tests might take a very long time to execute 2. The test will fail if the data is not the same as what the test expects
  2. If there is a change to the expected data, it can cause issues for existing tests
  3. Test results are dependent on how often your data receives updates (e.g., daily, weekly, monthly)

How can you build a data driven testing framework?

1) Gather real-world use cases from users
2) Collect functional requirements related to each use case
3) Create individual assertions for each requirement
4) Create variables that are used in multiple assertions


The main benefit of Data Driven Testing is being able to test multiple conditions using one script without much effort.

However, with this type of testing, it is common to need to make changes when the data used for testing changes. Since all code is dependent on data, if any of the tests are run in an environment with different data than what was originally tested with then it can cause failures.

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