Auger is a project to automatically generate unit tests for Python code.
Install auger with:
pip install auger-python
To generate a unit test for any class or module, for Python 2 or 3, do this:
import auger with auger.magic([ <any list of modules or classes> ]): <any code that exercises your application>
Here is a simple example that does not rely on Auger at all:
class Foo: # Declare a class with a method def bar(self, x): return 2 * x # Duplicate x and return it def main(): foo = Foo() # Create an instance of Foo print(foo.bar(32)) # Call the bar method and print the result main()
main function we call the
bar method and it will print 64.
To generate a unit test for this class, we run the code again, but this time in the context of Auger:
import auger with auger.magic([Foo]): main()
This will print out the following:
64 Auger: generated test: tests/test_Foo.py
The test that is generated looks like this, with some imports and test for main removed:
import unittest class FooTest(unittest.TestCase): def test_bar(self): foo_instance = Foo() self.assertEquals( foo_instance.bar(x=32), 64 ) if __name__ == "__main__": unittest.main()
Rather than emit tests in the file system, Auger can also print out the test to the console,
by using the
import auger with auger.magic([Foo], verbose=True): main()
In that case, Auger will not generate any tests, but just print them out.
Consider the following example,
pet.py, included in the
sample folder, that lets us create a
Pet with a name and a species:
from animal import Animal class Pet(Animal): def __init__(self, name, species): Animal.__init__(self, species) self.name = name def getName(self): return self.name def __str__(self): return "%s is a %s" % (self.getName(), self.getSpecies()) def createPet(name, species): return Pet(name, species)
Pet is really a special kind of
Animal, with a name, which is defined in
class Animal(object): def __init__(self, species): self.species = species def getSpecies(self): return self.species
With those two definitions, we can create a
Pet instance and print out some details:
import animal import pet def main(): p = pet.createPet("Polly", "Parrot") print(p, p.getName(), p.getSpecies()) main()
Polly is a Parrot Polly Parrot
With Auger, we can record all calls to all functions and methods defined in
while also remembering the details for all calls going out from
pet.py to other modules,
so they can be mocked out.
Instead of saying:
if __name__ == "__main__": main()
We would say:
import auger if __name__ == "__main__": with auger.magic([pet]): # this is the new line and invokes Auger main()
This produces the following automatically generated unit test for
from mock import patch from sample.animal import Animal import sample.pet from sample.pet import Pet import unittest class PetTest(unittest.TestCase): def test___str__(self, mock_get_age, mock_get_species): mock_get_age.return_value = 12 mock_get_species.return_value = 'Dog' pet_instance = Pet('Clifford', 'Dog', 12) self.assertEquals(pet_instance.__str__(), 'Clifford is a dog aged 12') def test_create_pet(self): self.assertIsInstance(sample.pet.create_pet(age=12,species='Dog',name='Clifford'), Pet) def test_get_name(self): pet_instance = Pet('Clifford', 'Dog', 12) self.assertEquals(pet_instance.get_name(), 'Clifford') def test_lower(self): self.assertEquals(Pet.lower(s='Dog'), 'dog') if __name__ == "__main__": unittest.main()
Note that auger detects object creation, method invocation, and static methods. It automatically
generate mocks for
Animal. The mock for
get_species returns 'Dog' and
get_age returns 12.
Namely, those were the values Auger recorded when we ran our sample code the last time.
By automatically generating unit tests, we dramatically cut down the cost of software development. The tests themselves are intended to help developers get going on their unit testing and lower the learning curve for how to write tests.
Auger does not do try to substitue parameters with synthetic values such as
Auger also does not act well when code uses exceptions. Auger also does not like methods that have a decorator.
Auger only records a given execution run and saves the run as a test. Auger does not know if the code actually works as intended. If the code contains a bug, Auger will simply record the buggy behavior. There is no free lunch here. It is up to the developer to verify the code actually works.