How I often approach this is to 1) write a couple of unit tests 2) refactor unit tests into data-driven tests, using pytest.mark.parametrize 3) use hypothesis t

How I often approach this is to 1) write a couple of unit tests 2) refactor unit tests into data-driven tests, using pytest.mark.parametrize 3) use hypothesis to generate the data. I very often get to 2, but not always to 3. The most basic things to check - No valid inputs give an error - All invalid inputs give a error These are enhanced if you sprinkle your code with assertions. Writing assertions inside the code is good training to identify. Something supposed always non-negative, check it. Something always supposed to have more than 0 entries, check it. Something supposed to be sorted, check a[0] <= a[1] and a[-2] <= a[-1]. Often you can test symmetry type properties - deserialize(serialize(in)) == in. - init(); connect(); disconnect(); -> state identical as just init(); Or you can use an 'oracle', something external that tells you whether the output is correct or not. - allTricksOptimized(in) == simpleReferenceImplementation(in) - myImplementation(in) == thirdparty.implementation(in)