Data driven testing is a powerful as well as scalable technique for test automation. It helps address many limitations of traditional hardcoded automation approaches. This article discusses the top 5 benefits of adopting a data driven testing methodology for test automation projects. It explains how separating test data from scripts improves code quality, reduces maintenance efforts along with enhancing overall testing capabilities.
- Reduced Test Code Maintenance
In traditional test automation approaches, test data is hard coded within test scripts. This makes test scripts brittle as well as any change in test data requires code changes. In data driven testing, test data is externalized so test scripts do not need to be changed even if test data changes. This significantly reduces test code maintenance effort as test scripts do not need recompilation for data changes. Testers can simply update the external data source without touching test code.
- Increased Test Coverage
With data driven testing, the same test can be run multiple times by simply changing the input data values in the external data source. This allows testers to cover more test scenarios along with edge cases without writing additional test code. For example, a login test can be run for different user types like admin, customer etc. by changing the user credentials in the data source. This improves test coverage without increasing test code.
- Improved Test Readability and Maintainability
When test data is externalized from test scripts, it makes test scripts more readable as well as maintainable. Test scripts focus only on test logic flow without clutter of data values. New testers can easily understand the test flow as well as logic. It also becomes easier to on-board new testers as test scripts do not need to be changed for data changes. Overall, it improves code quality along with maintainability of test automation suite.
- Parallel Test Execution
Data driven tests are highly scalable as well as support parallel test execution. Since each test run uses a unique data set, tests can be distributed across different environments/machines for parallel execution. This significantly reduces total test execution time. For example, a login test validating 10 different user types can be split across 2 machines with 5 users each to halve the execution time.
- Easy Integration with CI/CD
Data driven tests integrate seamlessly with continuous integration/delivery pipelines. Since test data is external, it is easy to parameterize tests to run against different environments like dev, test, staging etc. by simply changing the data source connection details. Test results as well as data sources can also be integrated with reporting tools for detailed analytics. This allows automated testing to be part of SDLC/DevOps processes.
Conclusion
Data driven framework helps improve test automation code quality, along with reduce maintenance efforts as well as improve overall testing capabilities like coverage, along with scalability as well as integration with SDLC processes. It is a more sustainable along with efficient approach for test automation compared to traditional hardcoded test scripts.
