Multiplying Python Unit Test Cases with Different Sets of Data

…or Data-Driven Tests

Often I find myself in the situation of wanting to run some unit test for a part of my system with different data values. I want to verify that the system works correctly with some combinations of data – and not just have a single good case test with a specific combination of parameters (which is often what people are contented with).

I do not want to create a test that loops over the test data, exercises the code, and verifies the correct execution with some assertions… that would make for a single test case that would fail at the first data combination that doesn’t work. I want it to be run as one test case per test data value.

The typical naive approach to this is to write a method that runs the actual test and performs assertions on the results (say verify_xxx_works), and create some test_xxx_* methods that call that one with different data values. Boy is that lame.

Fortunately, nose includes the concept of test functions as generators. If you write your test as a generator that spits out tuples of (callable, argument...), nose will call it and run one test case per yielded value, thus effectively multiplying your number of test cases. You will see OKs for values that pass, and one failure per each set of arguments that fails. Great!

Oops, wait, not so great. If you read the docs carefully (I didn’t on my first try) you will find the small print: Please note that method generators are not supported in unittest.TestCase subclasses

Meaning that if your tests are written using unittest.TestCase you’re on your own again.

Unhappy with the situation of not being able to run one TestCase method with different sets of data in a non-clumsy way, I’ve been playing around and the result is a small library that I’ve called “DDT” (which could either stand for Data-Driven or Data-Decorated Tests). It allows developers to create multiple test cases from a single test method, feeding it different data using a decorator.
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