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Did you ever think that most of your test functions were actually the same test code, but with different data inputs and expected results/exceptions? - pytest-cases leverages pytest and its great @pytest.mark.parametrize decorator, so that you can separate your test cases from your test functions. - In addition, pytest-cases provides several useful goodies to empower pytest. In particular it improves the fixture mechanism to support "fixture unions". This is a major change in the internal pytest engine, unlocking many possibilities such as using fixture references as parameter values in a test function. See here. pytest-cases is fully compliant with pytest-harvest so you can easily monitor the execution times and created artifacts. With it, it becomes very easy to create a complete data science benchmark, for example comparing various models on various datasets.
14 lines
886 B
Text
14 lines
886 B
Text
Did you ever think that most of your test functions were actually the same test
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code, but with different data inputs and expected results/exceptions?
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|
- pytest-cases leverages pytest and its great @pytest.mark.parametrize
|
|
decorator, so that you can separate your test cases from your test functions.
|
|
- In addition, pytest-cases provides several useful goodies to empower pytest.
|
|
In particular it improves the fixture mechanism to support "fixture unions".
|
|
This is a major change in the internal pytest engine, unlocking many
|
|
possibilities such as using fixture references as parameter values in a test
|
|
function. See here.
|
|
|
|
pytest-cases is fully compliant with pytest-harvest so you can easily monitor
|
|
the execution times and created artifacts. With it, it becomes very easy to
|
|
create a complete data science benchmark, for example comparing various models
|
|
on various datasets.
|