This section covers the new and improved data test support that was released with Kotest 4.3.0. To view the documentation for the previous data test support, click here
When writing tests that are logic based, one or two directed examples that work through specific scenarios make sense. Other times we have tests that are example based, and we want to provide many combinations of parameters.
In these situations, data driven testing (also called table driven testing) is an easy technique to avoid tedious boilerplate.
Kotest has support for data driven testing built into the framework. This means it will automatically generate the test case entries, based off input values provided by you.
Let's consider writing tests for a pythagorean triple function that returns true if the input values are valid triples.
We start by writing a data class that will hold each row of values.
Next we invoke the function
forAll inside a test case, passing in one or more of these data class rows, and a lambda
that performs some test logic for a given row.
Because we are using data classes, the input row can be destructured into the member properties.
Data driven testing can be used within any spec but must always be invoked inside a container test.
Kotest will automatically generate a test case for each input row, as if you had manually written a seperate test case for each.
If there is an error for any particular input row, then the test will fail and Kotest will output the values that
failed. For example, if we change the previous example to include the row
PythagTriple(5, 4, 3)
then that test will be marked as a failure.
The error message will contain the error and the input row details:
Test failed for (a, 5), (b, 4), (c, 3) expected:<9> but was:<41>
There is the inverse of
forNone which verifies that none of the rows pass the test.
By default, the name of each test is simply the
toString() on the input row. However we can customize this if we wish,
by passing in test names into the forAll / forNone functions.
Note that currently, you must also specify the data class type.
The output from this example is now slightly clearer:
Whether this is worth the extra effort depends on how readable the toString() method is on the data classes you are using.