We consider "testing" as a set of operations. But that's not completely true. The operations, either by us, or by the system itself, move different types of data around, and change the system's state.
The system's data and state are crucial for testing.
In order to learn about the quality of the product, we need to understand how data travels in the system. Without proper initialization, we wouldn't know our starting point. At the end of the operations, we need to understand how the system’s data and state have changed. And in order to confirm if the resulting data is correct, we need to prepare sets in advance to compare.
In a nutshell, without understanding data, our testing results are meaningless.
In this talk, the first session of our Data Modeling deep dive, we'll talk about systems' data and state. We'll learn what constitutes them, and what we can do to control them.
We'll see how they naturally weave into our tests, and see examples of data preparation and state control. We'll see how they can be useful for setting systems to test at the right point, for the right test.
