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Why data-driven automation can be a good choice
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Data-driven testing is the practice of keeping the execution script separate from the test data. In other words, you will have a single automation script that will reference a data table containing multiple rows with scenarios. |
When we talk about prioritizing tests for automation, it often comes down to high repetitiveness of actions and high number of iterations. Data-driven testing fits that pattern well and helps with some of the adoption barriers:
It removes the need to create an individual script for each test case, which is typically a time-consuming process. But often more importantly, it simplifies and accelerates maintenance because most changes to the test script do not affect the test data, and vice versa. Scaling execution up or down is also more flexible.
Info As an added bonus, if you need to switch frameworks/languages, your data sources would still be valid, and not as many scripts would need to be replaced.
- It can make collaboration easier since reviewing a data table does not require special technical skills. That, in turn, accelerates the overall process (since any subject matter expert can add iterations) and increases test data accuracy. The reporting can be set up to clearly reflect the status of each iteration, just as it would for separate scripts.
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