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8.1 Testing

Testing Rocks, Debugging Sucks

The dynamic nature of Python makes testing critically important to most applications. There is no compiler to find your bugs. The only way to find bugs is to run the code and make sure you try out all of its features.

Assertions

The assert statement is an internal check for the program. If an expression is not true, it raises a AssertionError exception.

assert statement syntax.

assert <expression> [, 'Diagnostic message']

For example.

assert isinstance(10, int), 'Expected int'

It shouldn't be used to check the user-input (i.e., data entered on a web form or something). It's purpose is more for internal checks and invariants (conditions that should always be true).

Contract Programming

Also known as Design By Contract, liberal use of assertions is an approach for designing software. It prescribes that software designers should define precise interface specifications for the components of the software.

For example, you might put assertions on all inputs of a function.

def add(x, y):
    assert isinstance(x, int), 'Expected int'
    assert isinstance(y, int), 'Expected int'
    return x + y

Checking inputs will immediately catch callers who aren't using appropriate arguments.

>>> add(2, 3)
5
>>> add('2', '3')
Traceback (most recent call last):
...
AssertionError: Expected int
>>>

Inline Tests

Assertions can also be used for simple tests.

def add(x, y):
    return x + y

assert add(2,2) == 4

This way you are including the test in the same module as your code.

Benefit: If the code is obviously broken, attempts to import the module will crash.

This is not recommended for exhaustive testing. It's more of a basic "smoke test". Does the function work on any example at all? If not, then something is definitely broken.

`unittest` Module

Suppose you have some code.

# simple.py

def add(x, y):
    return x + y

Now, suppose you want to test it. Create a separate testing file like this.

# test_simple.py

import simple
import unittest

Then define a testing class.

# test_simple.py

import simple
import unittest

# Notice that it inherits from unittest.TestCase
class TestAdd(unittest.TestCase):
    ...

The testing class must inherit from unittest.TestCase.

In the testing class, you define the testing methods.

# test_simple.py

import simple
import unittest

# Notice that it inherits from unittest.TestCase
class TestAdd(unittest.TestCase):
    def test_simple(self):
        # Test with simple integer arguments
        r = simple.add(2, 2)
        self.assertEqual(r, 5)
    def test_str(self):
        # Test with strings
        r = simple.add('hello', 'world')
        self.assertEqual(r, 'helloworld')

*Important: Each method must start with test.

Using `unittest`

There are several built in assertions that come with unittest. Each of them asserts a different thing.

# Assert that expr is True
self.assertTrue(expr)

# Assert that x == y
self.assertEqual(x,y)

# Assert that x != y
self.assertNotEqual(x,y)

# Assert that x is near y
self.assertAlmostEqual(x,y,places)

# Assert that callable(arg1,arg2,...) raises exc
self.assertRaises(exc, callable, arg1, arg2, ...)

This is not an exhaustive list. There are other assertions in the module.

Running `unittest`

To run the tests, turn the code into a script.

# test_simple.py

...

if __name__ == '__main__':
    unittest.main()

Then run Python on the test file.

bash % python3 test_simple.py
F.
========================================================
FAIL: test_simple (__main__.TestAdd)
--------------------------------------------------------
Traceback (most recent call last):
  File "testsimple.py", line 8, in test_simple
    self.assertEqual(r, 5)
AssertionError: 4 != 5
--------------------------------------------------------
Ran 2 tests in 0.000s
FAILED (failures=1)

Commentary

Effective unit testing is an art and it can grow to be quite complicated for large applications.

The unittest module has a huge number of options related to test runners, collection of results and other aspects of testing. Consult the documentation for details.

Third Party Test Tools

The built-in unittest module has the advantage of being available everywhere--it's part of Python. However, many programmers also find it to be quite verbose. A popular alternative is pytest. With pytest, your testing file simplifies to something like the following:

# test_simple.py
import simple

def test_simple():
    assert simple.add(2,2) == 4

def test_str():
    assert simple.add('hello','world') == 'helloworld'

To run the tests, you simply type a command such as python -m pytest. It will discover all of the tests and run them.

There's a lot more to pytest than this example, but it's usually pretty easy to get started should you decide to try it out.

Exercises

In this exercise, you will explore the basic mechanics of using Python's unittest module.

In earlier exercises, you wrote a file stock.py that contained a Stock class. For this exercise, it assumed that you're using the code written for Exercise 7.9 involving typed-properties. If, for some reason, that's not working, you might want to copy the solution from Solutions/7_9 to your working directory.

Exercise 8.1: Writing Unit Tests

In a separate file test_stock.py, write a set a unit tests for the Stock class. To get you started, here is a small fragment of code that tests instance creation:

# test_stock.py

import unittest
import stock

class TestStock(unittest.TestCase):
    def test_create(self):
        s = stock.Stock('GOOG', 100, 490.1)
        self.assertEqual(s.name, 'GOOG')
        self.assertEqual(s.shares, 100)
        self.assertEqual(s.price, 490.1)

if __name__ == '__main__':
    unittest.main()

Run your unit tests. You should get some output that looks like this:

.
----------------------------------------------------------------------
Ran 1 tests in 0.000s

OK

Once you're satisfied that it works, write additional unit tests that check for the following:

Make sure the s.cost property returns the correct value (49010.0)
Make sure the s.sell() method works correctly. It should decrement the value of s.shares accordingly.
Make sure that the s.shares attribute can't be set to a non-integer value.

For the last part, you're going to need to check that an exception is raised. An easy way to do that is with code like this:

class TestStock(unittest.TestCase):
    ...
    def test_bad_shares(self):
         s = stock.Stock('GOOG', 100, 490.1)
         with self.assertRaises(TypeError):
             s.shares = '100'