Data driven testing aims to solve two critical challenges of test automation – redundancy of test cases and maintenance of an extensive repository of test scripts and input data.
As your product functionality evolves, the test cases must be updated simultaneously for the test results to hold. When test data is hard-coded into scripts, incorporating the changes lead to a considerable increase in repetitive coding (especially for regression testing) and, as a result, makes these tests more prone to errors.
On the other hand, not incorporating the changes is also not a desirable scenario since it leads to the breaking of tests or an increase in false negatives. As a result, a lot of mundane maintenance work is done for these tests to be run for multiple data sets and by different cross-functional teams.
This blog post will provide a detailed explanation of how data driven testing optimises this aspect of testing.
What is Data Driven Testing?
Data driven testing framework separates test scripts from test data. So you can use a single script to validate multiple data sets. This increases test coverage for a given scenario without writing a unique test logic for every data.
While it may appear as a small feat when viewed from a program level, it contributes to saving testing time and resources incrementally and augmenting testing efficiency.
Imagine a testing approach wherein test data is hard-coded into test scripts. For scenarios where multiple data need to be tested, different versions of the same scripts must be coded to cover all the data sets. So, QA may choose to
- Either go for complete test coverage and code individual test scripts for every data: More time and resources are spent on coding and maintaining multiple copies of the redundant test scripts by the testing team.
- Or aim for faster releases at the cost of deeper coverage and add to technical debt: lesser test coverage and a more significant amount of bug leakage into production.
Data driven testing framework helps testing teams meet the competing demands of faster releases and deeper test coverage by accelerating testing time for scenarios requiring variable data to be validated. It also lets testing teams modify test scripts and test data independently, thus making maintenance of test cases a cakewalk in the face of rapidly evolving product and testing scenarios.
When should you opt for data driven testing
Data driven testing will be most useful when you use it for
- Regression testing
- Test scripts in which data gets modified frequently
- Tests that are repeated for multiple data sets
- Setting up test scripts that different teams for multiple data sets can reuse
As you can see, data driven testing is for specific scenarios that, when not addressed, might increase test case redundancy, difficulty in test maintenance and wasteful usage of resources. This becomes especially critical when trying to scale your STLC while simultaneously optimising testing by reducing wasteful deployment of resources.
Benefits of data driven testing
Data driven testing framework aims to simplify the maintenance and modification of test cases. As test scripts and data are stored separately, scripts become reusable, and tests become simpler to execute and maintain. This also enables teams to update test logic and data independently, thereby reducing the repetitive coding work that hard-coded scripts involve in case of any test data or product functionality change. This separation also has other far-reaching impacts on different aspects of STLC.
Tactical-level benefits of data driven testing
- Improves reusability of test scripts
- Improves test coverage
- Reduces test case redundancy
- Easy maintenance of test cases
Program-level benefits of data driven testing
- More efficient usage of coding resources
- Saves cost on testing while improving quality with better test coverage
- Script reusability across teams brings in better alignment and coordination
McKinsey outlined in the data driven enterprise of 2025 that organisations that leverage data faster to optimise their processes and aid decision-making will gain the most from data-supported capabilities. Among the seven characteristics defined for a data driven enterprise in this report, the most significant was the capability to embed innovative data techniques to accelerate workflows, resolve challenges faster and free up employees to focus more on “human” domains such as innovation, collaboration and communication. That’s precisely what data driven testing aims to achieve for teams.
Keen on implementing data driven testing for your STLC? Write to us at [email protected].