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      How To Use unittest to Write a Test Case for a Function in Python


      The author selected the COVID-19 Relief Fund to receive a donation as part of the Write for DOnations program.

      Introduction

      The Python standard library includes the unittest module to help you write and run tests for your Python code.

      Tests written using the unittest module can help you find bugs in your programs, and prevent regressions from occurring as you change your code over time. Teams adhering to test-driven development may find unittest useful to ensure all authored code has a corresponding set of tests.

      In this tutorial, you will use Python’s unittest module to write a test for a function.

      Prerequisites

      To get the most out of this tutorial, you’ll need:

      Defining a TestCase Subclass

      One of the most important classes provided by the unittest module is named TestCase. TestCase provides the general scaffolding for testing our functions. Let’s consider an example:

      test_add_fish_to_aquarium.py

      import unittest
      
      def add_fish_to_aquarium(fish_list):
          if len(fish_list) > 10:
              raise ValueError("A maximum of 10 fish can be added to the aquarium")
          return {"tank_a": fish_list}
      
      
      class TestAddFishToAquarium(unittest.TestCase):
          def test_add_fish_to_aquarium_success(self):
              actual = add_fish_to_aquarium(fish_list=["shark", "tuna"])
              expected = {"tank_a": ["shark", "tuna"]}
              self.assertEqual(actual, expected)
      

      First we import unittest to make the module available to our code. We then define the function we want to test—here it is add_fish_to_aquarium.

      In this case our add_fish_to_aquarium function accepts a list of fish named fish_list, and raises an error if fish_list has more than 10 elements. The function then returns a dictionary mapping the name of a fish tank "tank_a" to the given fish_list.

      A class named TestAddFishToAquarium is defined as a subclass of unittest.TestCase. A method named test_add_fish_to_aquarium_success is defined on TestAddFishToAquarium. test_add_fish_to_aquarium_success calls the add_fish_to_aquarium function with a specific input and verifies that the actual returned value matches the value we’d expect to be returned.

      Now that we’ve defined a TestCase subclass with a test, let’s review how we can execute that test.

      Executing a TestCase

      In the previous section, we created a TestCase subclass named TestAddFishToAquarium. From the same directory as the test_add_fish_to_aquarium.py file, let’s run that test with the following command:

      • python -m unittest test_add_fish_to_aquarium.py

      We invoked the Python library module named unittest with python -m unittest. Then, we provided the path to our file containing our TestAddFishToAquarium TestCase as an argument.

      After we run this command, we receive output like the following:

      Output

      . ---------------------------------------------------------------------- Ran 1 test in 0.000s OK

      The unittest module ran our test and told us that our test ran OK. The single . on the first line of the output represents our passed test.

      Note: TestCase recognizes test methods as any method that begins with test. For example, def test_add_fish_to_aquarium_success(self) is recognized as a test and will be run as such. def example_test(self), conversely, would not be recognized as a test because it does not begin with test. Only methods beginning with test will be run and reported when you run python -m unittest ....

      Now let’s try a test with a failure.

      We modify the following highlighted line in our test method to introduce a failure:

      test_add_fish_to_aquarium.py

      import unittest
      
      def add_fish_to_aquarium(fish_list):
          if len(fish_list) > 10:
              raise ValueError("A maximum of 10 fish can be added to the aquarium")
          return {"tank_a": fish_list}
      
      
      class TestAddFishToAquarium(unittest.TestCase):
          def test_add_fish_to_aquarium_success(self):
              actual = add_fish_to_aquarium(fish_list=["shark", "tuna"])
              expected = {"tank_a": ["rabbit"]}
              self.assertEqual(actual, expected)
      

      The modified test will fail because add_fish_to_aquarium won’t return "rabbit" in its list of fish belonging to "tank_a". Let’s run the test.

      Again, from the same directory as test_add_fish_to_aquarium.py we run:

      • python -m unittest test_add_fish_to_aquarium.py

      When we run this command, we receive output like the following:

      Output

      F ====================================================================== FAIL: test_add_fish_to_aquarium_success (test_add_fish_to_aquarium.TestAddFishToAquarium) ---------------------------------------------------------------------- Traceback (most recent call last): File "test_add_fish_to_aquarium.py", line 13, in test_add_fish_to_aquarium_success self.assertEqual(actual, expected) AssertionError: {'tank_a': ['shark', 'tuna']} != {'tank_a': ['rabbit']} - {'tank_a': ['shark', 'tuna']} + {'tank_a': ['rabbit']} ---------------------------------------------------------------------- Ran 1 test in 0.001s FAILED (failures=1)

      The failure output indicates that our test failed. The actual output of {'tank_a': ['shark', 'tuna']} did not match the (incorrect) expectation we added to test_add_fish_to_aquarium.py of: {'tank_a': ['rabbit']}. Notice also that instead of a ., the first line of the output now has an F. Whereas . characters are outputted when tests pass, F is the output when unittest runs a test that fails.

      Now that we’ve written and run a test, let’s try writing another test for a different behavior of the add_fish_to_aquarium function.

      Testing a Function that Raises an Exception

      unittest can also help us verify that the add_fish_to_aquarium function raises a ValueError Exception if given too many fish as input. Let’s expand on our earlier example, and add a new test method named test_add_fish_to_aquarium_exception:

      test_add_fish_to_aquarium.py

      import unittest
      
      def add_fish_to_aquarium(fish_list):
          if len(fish_list) > 10:
              raise ValueError("A maximum of 10 fish can be added to the aquarium")
          return {"tank_a": fish_list}
      
      
      class TestAddFishToAquarium(unittest.TestCase):
          def test_add_fish_to_aquarium_success(self):
              actual = add_fish_to_aquarium(fish_list=["shark", "tuna"])
              expected = {"tank_a": ["shark", "tuna"]}
              self.assertEqual(actual, expected)
      
          def test_add_fish_to_aquarium_exception(self):
              too_many_fish = ["shark"] * 25
              with self.assertRaises(ValueError) as exception_context:
                  add_fish_to_aquarium(fish_list=too_many_fish)
              self.assertEqual(
                  str(exception_context.exception),
                  "A maximum of 10 fish can be added to the aquarium"
              )
      

      The new test method test_add_fish_to_aquarium_exception also invokes the add_fish_to_aquarium function, but it does so with a 25 element long list containing the string "shark" repeated 25 times.

      test_add_fish_to_aquarium_exception uses the with self.assertRaises(...) context manager provided by TestCase to check that add_fish_to_aquarium rejects the inputted list as too long. The first argument to self.assertRaises is the Exception class that we expect to be raised—in this case, ValueError. The self.assertRaises context manager is bound to a variable named exception_context. The exception attribute on exception_context contains the underlying ValueError that add_fish_to_aquarium raised. When we call str() on that ValueError to retrieve its message, it returns the correct exception message we expected.

      From the same directory as test_add_fish_to_aquarium.py, let’s run our test:

      • python -m unittest test_add_fish_to_aquarium.py

      When we run this command, we receive output like the following:

      Output

      .. ---------------------------------------------------------------------- Ran 2 tests in 0.000s OK

      Notably, our test would have failed if add_fish_to_aquarium either didn’t raise an Exception, or raised a different Exception (for example TypeError instead of ValueError).

      Note: unittest.TestCase exposes a number of other methods beyond assertEqual and assertRaises that you can use. The full list of assertion methods can be found in the documentation, but a selection are included here:

      Method Assertion
      assertEqual(a, b) a == b
      assertNotEqual(a, b) a != b
      assertTrue(a) bool(a) is True
      assertFalse(a) bool(a) is False
      assertIsNone(a) a is None
      assertIsNotNone(a) a is not None
      assertIn(a, b) a in b
      assertNotIn(a, b) a not in b

      Now that we’ve written some basic tests, let’s see how we can use other tools provided by TestCase to harness whatever code we are testing.

      Using the setUp Method to Create Resources

      TestCase also supports a setUp method to help you create resources on a per-test basis. setUp methods can be helpful when you have a common set of preparation code that you want to run before each and every one of your tests. setUp lets you put all this preparation code in a single place, instead of repeating it over and over for each individual test.

      Let’s take a look at an example:

      test_fish_tank.py

      import unittest
      
      class FishTank:
          def __init__(self):
              self.has_water = False
      
          def fill_with_water(self):
              self.has_water = True
      
      class TestFishTank(unittest.TestCase):
          def setUp(self):
              self.fish_tank = FishTank()
      
          def test_fish_tank_empty_by_default(self):
              self.assertFalse(self.fish_tank.has_water)
      
          def test_fish_tank_can_be_filled(self):
              self.fish_tank.fill_with_water()
              self.assertTrue(self.fish_tank.has_water)
      

      test_fish_tank.py defines a class named FishTank. FishTank.has_water is initially set to False, but can be set to True by calling FishTank.fill_with_water(). The TestCase subclass TestFishTank defines a method named setUp that instantiates a new FishTank instance and assigns that instance to self.fish_tank.

      Since setUp is run before every individual test method, a new FishTank instance is instantiated for both test_fish_tank_empty_by_default and test_fish_tank_can_be_filled. test_fish_tank_empty_by_default verifies that has_water starts off as False. test_fish_tank_can_be_filled verifies that has_water is set to True after calling fill_with_water().

      From the same directory as test_fish_tank.py, we can run:

      • python -m unittest test_fish_tank.py

      If we run the previous command, we will receive the following output:

      Output

      .. ---------------------------------------------------------------------- Ran 2 tests in 0.000s OK

      The final output shows that the two tests both pass.

      setUp allows us to write preparation code that is run for all of our tests in a TestCase subclass.

      Note: If you have multiple test files with TestCase subclasses that you’d like to run, consider using python -m unittest discover to run more than one test file. Run python -m unittest discover --help for more information.

      Using the tearDown Method to Clean Up Resources

      TestCase supports a counterpart to the setUp method named tearDown. tearDown is useful if, for example, we need to clean up connections to a database, or modifications made to a filesystem after each test completes. We’ll review an example that uses tearDown with filesystems:

      test_advanced_fish_tank.py

      import os
      import unittest
      
      class AdvancedFishTank:
          def __init__(self):
              self.fish_tank_file_name = "fish_tank.txt"
              default_contents = "shark, tuna"
              with open(self.fish_tank_file_name, "w") as f:
                  f.write(default_contents)
      
          def empty_tank(self):
              os.remove(self.fish_tank_file_name)
      
      
      class TestAdvancedFishTank(unittest.TestCase):
          def setUp(self):
              self.fish_tank = AdvancedFishTank()
      
          def tearDown(self):
              self.fish_tank.empty_tank()
      
          def test_fish_tank_writes_file(self):
              with open(self.fish_tank.fish_tank_file_name) as f:
                  contents = f.read()
              self.assertEqual(contents, "shark, tuna")
      

      test_advanced_fish_tank.py defines a class named AdvancedFishTank. AdvancedFishTank creates a file named fish_tank.txt and writes the string "shark, tuna" to it. AdvancedFishTank also exposes an empty_tank method that removes the fish_tank.txt file. The TestAdvancedFishTank TestCase subclass defines both a setUp and tearDown method.

      The setUp method creates an AdvancedFishTank instance and assigns it to self.fish_tank. The tearDown method calls the empty_tank method on self.fish_tank: this ensures that the fish_tank.txt file is removed after each test method runs. This way, each test starts with a clean slate. The test_fish_tank_writes_file method verifies that the default contents of "shark, tuna" are written to the fish_tank.txt file.

      From the same directory as test_advanced_fish_tank.py let’s run:

      • python -m unittest test_advanced_fish_tank.py

      We will receive the following output:

      Output

      . ---------------------------------------------------------------------- Ran 1 test in 0.000s OK

      tearDown allows you to write cleanup code that is run for all of your tests in a TestCase subclass.

      Conclusion

      In this tutorial, you have written TestCase classes with different assertions, used the setUp and tearDown methods, and run your tests from the command line.

      The unittest module exposes additional classes and utilities that you did not cover in this tutorial. Now that you have a baseline, you can use the unittest module’s documentation to learn more about other available classes and utilities. You may also be interested in How To Add Unit Testing to Your Django Project.



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