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Code Testing

  • smoke tests

  • unit tests

  • pytest

Clicker Question #1

Given the following code, which assert will fail?

def extend(input_arg):
    output = input_arg.copy()
    for element in input_arg:
        output.append(element)
    return output

# test here
extend([1,2,3,4])

[1, 2, 3, 4, 1, 2, 3, 4]

  • A) assert isinstance(extend([1, 2]), list)

  • B) assert extend([1, 2]) == [1, 2, 1, 2]

  • C) assert extend((1, 2)) == (1, 2, 1, 2)

  • D) assert extend(['a', 'b', 'c']) == ['a', 'b', 'c', 'a', 'b', 'c']

  • E) assert extend([]) == []

Clicker Question - Asserts

# Check that extend returns a list
assert isinstance(extend([1, 2]), list)

# Check that an input list returns the expected result
assert extend([1, 2]) == [1, 2, 1, 2]

# Check if the function works on tuples
assert extend((1, 2)) == (1, 2, 1, 2)

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-c510c0c20682> in <module>()
      1 # Check if the function works on tuples
----> 2 assert extend((1, 2)) == (1, 2, 1, 2)

<ipython-input-1-a3ca5f93bd43> in extend(input_arg)
      1 def extend(input_arg):
----> 2     output = input_arg.copy()
      3     for element in input_arg:
      4         output.append(element)
      5     return output

AttributeError: 'tuple' object has no attribute 'copy'

# Check that a different input list (different lengths / contents) returns expected result
assert extend(['a', 'b', 'c']) == ['a', 'b', 'c', 'a', 'b', 'c']

# Check that an empty list executes, executing an empty list
assert extend([]) == []

Code Testing

Code tests is code that runs and checks other code, to make sure it does what it is expected to do.

How to Write Tests

Given a function or class you want to test:

  • You need to have an expectation for what it should do

  • Write out some example cases, with known answers

  • Use assert to check that your example cases do run as expected

  • Collect these examples into test functions, stored in test files

Why Write Tests

  • To ensure code does what it is supposed to

  • To have a system for checking things when you change things in the code

The Best (Laziest) Argument for Writing Tests

Whenever you write new code, you will find yourself using little snippets of code to check it.

Collect these snippets into a test function, and you get re-runnable tests for free.

Example Test Code

def add(num1, num2):
    return num1 + num2

add(2.7, 1.2) == 3.9

False

import math

def test_add():
    """Tests for the `add` function."""
    
    # Test adding positve numbers
    assert add(2, 2) == 4
    
    # Test adding negative numbers
    assert add(-2, -2) == -4
    
    # Test adding floats
    # assert add(2.7, 1.2) == 3.9
    assert math.isclose(add(2.7, 1.2), 3.9)
    
    # Test adding with 0
    assert add(2, 0) == 2

# Run our test function
test_add()

Clicker Question #2

# Given the following function:
def divide_list(in_list):    
    output = []
    
    for el1, el2 in zip(in_list[1:], in_list[0:-1]):
        output.append(el1 / el2)
    
    return output

divide_list((0,2,3))

---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-27-c91f35be7f5f> in <module>()
----> 1 divide_list((0,2,3))

<ipython-input-18-1f783ab343a3> in divide_list(in_list)
      4 
      5     for el1, el2 in zip(in_list[1:], in_list[0:-1]):
----> 6         output.append(el1 / el2)
      7 
      8     return output

ZeroDivisionError: division by zero

# And the following test function:
def test_divide_list():
    assert isinstance(divide_list([1, 2]), list)
    assert divide_list([1, 2, 4]) == [2, 2]

test_divide_list()

  • A) These tests will pass, and this function is well tested

  • B) These tests will pass, but this function needs more tests

  • C) These tests will fail, but they cover the needed cases

  • D) These tests will fail, and we should also have more tests

PyTest

PyTest is a module that for writing and running test code. It is available from Anaconda and datahub.

Levels of Code Testing:

  • Smoke Tests

  • Unit Tests

  • Integration Tests

  • System Tests

Test Driven Development

In software development, test-driven development is an approach in which you write tests first - and then write code to pass the tests.

Test Driven Development

  • Ensures you go into writing code with a good plan / outline

  • Ensures that you have a test suite, as you can not decide to neglect test code after the fact

  • Note: when you complete (or at least write) assignments for this class, you are effectively doing test-driven development

Test Coverage

Test coverage is the proportion of a software project that is run by the test suite.

Writing Good Code

All in all, write code that is:

  • Documented

  • Well organized (follows a style guide)

  • Tested

And you will have understandable, maintainable, and trustable code.

Why We Write Tests:

  • ensure does does what it’s supposed to

  • system for checking things when you change / make updates in the future

Tests, when run, help identify code that will give an error if something has gone wrong.

Four general types

  1. Smoke tests - preliminary tests to basic functionality; checks if something runs (but not necessarily if it does the right thing) (sanity check)

  2. Unit tests - test functions & objects to ensure that they code is behaving as expected

  3. Integration tests - tests functions, classes & modules interacting

  4. System tests - tests end-to-end behavior

Unit Tests

  • one test for each “piece” of your code (each function, each class, each module, etc)

  • passes silently if true

  • error if it fails

  • consider “edge cases”

  • help you resist the urge to assume computers will act how you think it will work

  • functions used with pytest start with test_

pytest

  • check if error raised when expected to be raised

  • autorun all of your tests

  • formal testing to your code/projects

Executing pytest

  1. Look for any file called test_...

  2. If everything works, silently moves along.

  3. For anything that fails, will alert you.

Available from Anaconda and on datahub

Clicker Question #3

Write a test function that checks the following piece of code:

  • A) I did it!

  • B) I think I did it!

  • C) I’m lost.

Thought process:

  1. Define test function def test_...

  2. make assertion within the test function

    • check that function sums the list (which was our expectation)

    • check that the input was a list (either in function or test function)

    • check the output is expected output / expected type

def sum_list(input_list):
    """add all values in a list - return sum"""
    
    output = 0
    
    for val in input_list:
        output += val
        
    return output

### YOUR TEST
def test_sum_list():
    assert sum_list([1, 2, 3]) == 6

test_sum_list()

### POSSIBLE TEST
def test_sum_list():
    
    # write multiple asserts
    assert callable(sum_list)
    assert isinstance(sum_list([1, 2, 3, 4]), int)
    assert sum_list([1, 2, 3, 4]) == 10
    
test_sum_list()
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