I. Testing a Python Function
1. Introduction to Function Testing
In the realm of software development, there's a popular saying: "If it's not tested, it's broken." This statement underscores the crucial role of testing. So, how do we apply testing in Python, specifically for functions? How many tests are adequate to ensure our function works as expected?
Testing is similar to a detective's investigation. They don't question just one suspect; they interrogate many to corroborate the story and ensure they're getting the truth. Similarly, we don't run just one test; we implement several to ascertain our function's correctness.
2. An Example Function: Splitting Training and Testing Sets
Let's dive into a practical example, which involves a function that we will call split_into_training_and_testing_sets(). This function behaves somewhat like a schoolmaster, dividing a class (or in this case, a two-dimensional NumPy array) into two groups: a larger one for training and a smaller one for testing. Here's a rough skeleton of what this function looks like:
def split_into_training_and_testing_sets(data):
# The function splits data into training and testing sets and returns them
pass
3. Test for Length Rather than Value
Imagine this function as a baker who separates dough into two parts: one for bread (training array) and a smaller one for pastries (testing array). The precise shape and size of the bread and pastries can vary, but the baker ensures that around 75% of the dough goes to bread and 25% to pastries.
So, when we test, we are more concerned about the amount of dough (the length of the array) rather than the specific pieces (actual values). We compute the lengths of the training and testing arrays like this:
def test_lengths(training, testing, data_length):
# Test if the lengths of the arrays are as expected
assert len(training) == int(0.75 * data_length)
assert len(testing) == data_length - len(training)
4. How to Test Arguments and Expected Return Values
To ensure our function works correctly, we test it using different arguments and check if it returns the expected values. For instance, if the input array has 8 rows, the training array should contain 6 rows and the testing array should contain 2 rows.
def test_return_values(training, testing):
assert len(training) == 6
assert len(testing) == 2
5. How Many Arguments Should Be Tested?
However, we can't test all possible inputs due to time and computational limitations. It's like asking a detective to question every person in the city; it's simply not feasible. So, we face the question: how many tests are enough?
6. Types of Arguments to Test
To answer this question, we adopt a structured approach, testing a few arguments from each of these categories:
Bad arguments: These are the mischief-makers, the ones that cause the function to throw a tantrum (raise an exception) instead of playing nice (returning a value).
Special arguments: These are the VIPs, the arguments that make the function behave in a special or different way.
Normal arguments: These are the common folks, arguments that are neither bad nor special.
Here's how we test each category:
# Testing bad arguments
def test_bad_arguments(func, bad_args):
for arg in bad_args:
try:
func(arg)
except Exception as e:
assert isinstance(e, ValueError)
# Testing special arguments
def test_special_arguments(func, special_args, expected):
for arg, exp in zip(special_args, expected):
assert func(arg) == exp
# Testing normal arguments
def test_normal_arguments(func, normal_args, expected):
for arg, exp in zip(normal_args, expected):
assert func(arg) == exp
7. Well-Tested Functions
If our function passes all these tests - dealing with the mischief-makers, accommodating the VIPs, and handling the common folks - then we can proudly declare it well-tested!
II. Test Driven Development
1. The Importance of Writing Unit Tests
Unit tests are often considered a 'second priority' and sometimes end up in the developer's graveyard of 'things to do tomorrow.' However, this mindset can lead to serious long-term consequences, like constructing a building on an untested foundation, leading to catastrophic failure.
2. Introduction to Test-Driven Development (TDD)
Enter Test-Driven Development (TDD), a development technique that asserts, "Test before you build." It's like an architect who sketches out a detailed plan before construction begins.
3. Writing Unit Tests Before Implementation
In TDD, we first write our tests, forcing us to consider all possible arguments and return values, including normal, special, and bad arguments. This careful planning helps us crystalize our function's requirements, making its implementation easier, like having a cooking recipe before starting to cook.
# This is a sample test for our future function, it doesn't exist yet
def test_split_into_training
_and_testing_sets():
pass
4. Practical Application of TDD
Let's consider a new function we need to create, convert_to_int(). In the spirit of TDD, we will first write our tests.
# Sample tests for the future function
def test_convert_to_int():
pass
5. The Steps of TDD
TDD follows a cyclical process: write the tests, watch them fail, write the function, then watch the tests pass. This cycle continues until the function behaves as expected for all tests.
# Defining the function
def convert_to_int():
pass
# Running the tests
test_convert_to_int()
If we've followed these steps carefully, we're now holding the golden key to efficient Python development - a well-tested function built through Test-Driven Development!
Conclusion
Python testing might seem like a steep mountain to climb, but once conquered, it offers an incredible view: the assurance that our code is reliable and robust. By adopting TDD and mastering functional testing, we can develop Python applications that stand tall and firm, like well-constructed skyscrapers, ready to face any challenges thrown their way. The journey of a thousand miles begins with a single step. In Python testing, that step is writing your first test!
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