Unit Testing
Open the python console (REPL) on the command line (terminal).
Python 3.8.5 (default, Jan 27 2021, 15:41:15)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>>
That is how mine looks like. On windows, if you can see something like this, then you have to go back to exercise 0 (Set up) and add the python to the environmental variable path.
assert
assert keyword raises an error, an AssertionError when the assertion fails. Consider the snippet below on how to use assert.
assert 1 == 1
The above assertion passes because 1 == 1 is True. There won't be an error since the assertion passed.
Try out for a false assertion.
Python 3.8.5 (default, Jan 27 2021, 15:41:15)
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> assert 1 == 2
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AssertionError
>>>
If you want to use assert in our code, we would have to try and except it - we have to catch the error raised. We discussed try and except in exercise 18 (Exceptions). Consider the code below where we catch the raised exception.
try:
assert 1 == 2
print("Hoo, we are safe now")
except:
print("Haa, we are not safe, man. The assertion failed!!")
# output
# Haa, we are not safe, man. The assertion failed!!
Have you seen a problem that we shall face when we work on huge or complicated projects? We should use a Unit testing module. Python comes with a unit testing module, unittest.
unittest module
Testing is an important part of software engineering. We do unit testing to check the correctness of our program - the individual components (functions). Usually, in most firms, unit tests are written before the code is written - this is known as Test-Driven Development. We shall write the tests after we write the code. We shall use the built-in unittest module. There are many testing concepts but we shall look at TestCase.
Some testing methods
| Method | Checks that |
|---|---|
assertEqual(a, b) |
a == b |
assertNotEqual(a, b) |
a != b |
assertTrue(x) |
bool(x) is True |
assertFalse(x) |
bool(x) is False |
assertIs(a, b) |
a is b |
assertNotIs(a, b) |
a is not b |
assertIsNone(x) |
x is None |
assertIsNotNone(x) |
x is not None |
assertIn(a, b) |
a in b |
assertNotIn(a, b) |
a not in b |
isinstance(a, b) |
type(a) == type(b) |
isNotinstance(a, b) |
type(a) != type(b) |
Example 1
Let's look at a basic example of a unit test. Our testing will be solely done on the individual functions that we have. We shall write a unit test to check if a function returns 1.
# test_one.py
import unittest
# extend the unittest.TestCase
class TestOne(unittest.TestCase):
def test_int_1_is_int_1(self):
self.assertEqual(1, 1)
def test_int_1_is_float_1_pt_0(self):
self.assertEqual(1, 1.0)
def test_int_1_is_str_1(self):
self.assertEqual(1, '1')
if __name__ == "__main__":
unittest.main()
The method names start with test. This indicates it is a test. Now to run the test, python3 test_one.py will work as well as python3 -m unittest test_one.py. There are instances where the latter is better. Where should the latter be used?
Example 2
Consider that we have a program that does some mathematical operations - addition, multiplication and division. We shall use the assertEqual and assertIsNone method to test if our methods are returning the same value as we expect.
# mathsy.py
class Mathsy:
def __init__(self, operator, first_operand, second_operand):
self.operator = operator
self.first_operand = first_operand
self.second_operand = second_operand
def add(self):
return self.first_operand + self.second_operand
def mult(self):
return self.first_operand * self.second_operand
def div(self):
if self.second_operand == 0:
return None
else:
return self.first_operand / self.second_operand
def evaluate(self):
if self.operator == '+':
return self.add()
elif self.operator == '*':
return self.mult()
elif self.operator == '/':
return self.div()
else:
raise Execption(f"{self.operator} not known")
Now the test.
# test.py
import unittest
from mathsy import Mathsy
# extend the unittest.TestCase
class MathsyTest(unittest.TestCase):
def test_add(self):
self.assertEqual(Mathsy('+', 2, 4).evaluate(), 6)
self.assertEqual(Mathsy('+', 1000, 4).evaluate(), 1004)
def test_mult(self):
self.assertEqual(Mathsy('*', 2, 4).evaluate(), 8)
self.assertEqual(Mathsy('*', 1000, 4).evaluate(), 4000)
def test_div(self):
self.assertEqual(Mathsy('/', 2, 4).evaluate(), 0.5)
self.assertEqual(Mathsy('/', 1000, 4).evaluate(), 250)
self.assertEqual(Mathsy('/', 0, 4).evaluate(), 0)
self.assertEqual(Mathsy('/', 4, 0).evaluate(), None)
# the expected value is None - so we check if
# it actually does return the None
self.assertIsNone(Mathsy('/', 4, 0).evaluate())
if __name__ == "__main__":
unittest.main()
Check this out about testing.
Practicals
Complete the program below. This is an implementation of a function that returns the factorial of a given integer value.
# practical.py
import unittest
def factorial(n: int) -> int:
pass
class TestFactorial(unittest.TestCase):
def test_zero_factorial_is_one(self):
pass
def test_one_factorial_is_one(self):
pass
def test_five_factorial_is_120(self):
pass
if __name__ == "__main__":
unittest.main()
Summary
- UnitTesting is very important in the world of software engineering
- It checks the correctness of our code
- to use pythons built-in unit testing package, import it,
import unittest - create a class,
ModuleTestand subclassunittest.TestCase - add
if __name__ == "__main__": unittest.main()to actually run the test when called.
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