TDD in Python with pytest - Part 1

This series of posts comes directly from my book Clean Architectures in Python. As I am reviewing the book to prepare a second edition, I realised that Harry Percival was right when he said that the initial part on TDD shouldn't be in the book. That's a prerequisite to follow the chapters on the clean architecture, but it is something many programmers already know and they might be surprised to find it in a book that discusses architectures.

So, I decided to move it here before I start working on a new version of the book. I also followed the advice of valorien, who pointed out that the main example had some bad naming choices, and so I reworked the code.

Introduction¶

Test-Driven Development (TDD) is fortunately one of the names that I can spot most frequently when people talk about methodologies. Unfortunately, many programmers still do not follow it, fearing that it will impose a further burden on the already difficult life of a developer.

In this chapter I will try to outline the basic concept of TDD and to show you how your job as a programmer can greatly benefit from it. I will develop a very simple project to show how to practically write software following this methodology.

TDD is a methodology, something that can help you to create better code. But it is not going to solve all your problems. As with all methodologies you have to pay attention not to commit blindly to it. Try to understand the reasons why certain practices are suggested by the methodology and you will also understand when and why you can or have to be flexible.

Keep also in mind that testing is a broader concept that doesn't end with TDD, which focuses a lot on unit testing, a specific type of test that helps you to develop the API of your library/package. There are other types of tests, like integration or functional ones, that are not specifically part of the TDD methodology, strictly speaking, even though the TDD approach can be extended to any testing activity.

A real-life example¶

Let's start with a simple example taken from a programmer's everyday life.

The programmer is in the office with other colleagues, trying to nail down an issue in some part of the software. Suddenly the boss storms into the office, and addresses the programmer:

Boss: I just met with the rest of the board. Our clients are not happy, we didn't fix enough bugs in the last two months.

Programmer: I see. How many bugs did we fix?

Boss: Well, not enough!

Programmer: OK, so how many bugs do we have to fix every month?

Boss: More!

I guess you feel very sorry for the poor programmer. Apart from the aggressive attitude of the boss, what is the real issue in this conversation? At the end of it there is no hint for the programmer and their colleagues about what to do next. They don't have any clue about what they have to change. They can definitely try to work harder, but the boss didn't refer to actual figures, so it will be definitely hard for the developers to understand if they improved "enough".

The classical sorites paradox may help to understand the issue. One of the standard formulations, taken from the Wikipedia page, is

Where is the issue? The concept expressed by the word "heap" is nebulous, it is not defined clearly enough to allow the process to find a stable point, or a solution.

When you write software you face that same challenge. You cannot conceive a function and just expect it "to work", because this is not clearly defined. How do you test if the function that you wrote "works"? What do you mean by "works"? TDD forces you to clearly state your goal before you write the code. Actually, the TDD mantra is "Test first, code later", which can be translated to "Goal first, solution later". Will shortly see a practical example of this.

For the time being, consider that this is a valid practice also outside the realm of software creation. Whoever runs a business knows that you need to be able to extract some numbers (KPIs) from the activity of your company, because it is by comparing those numbers with some predefined thresholds that you can easily tell if the business is healthy or not. KPIs are a form of test, and you have to define them in advance, according to the expectations or needs that you have.

Pay attention. Nothing prevents you from changing the thresholds as a reaction to external events. You may consider that, given the incredible heat wave that hit your country, the amount of coats that your company sold could not reach the goal. So, because of a specific event, you can justify a change in the test (KPI). If you didn't have the test you would have just generically recorded that you earned less money.

Going back to software and TDD, following this methodology you are forced to state clear goals like

sum(4, 5) == 9

Let me read this test for you: there will be a sum function available in the system that accepts two integers. If the two integers are 4 and 5 the function will return 9.

As you can see there are many things that are tested by this statement.

• The function exists and can be imported
• The function accepts two integers
• Passing 4 and 5 as inputs, the output of the function will be 9.

Pay attention that at this stage there is no code that implements the function sum, the tests will fail for sure.

As we will see with a practical example in the next chapter, what I explained in this section will become a set of rules of the methodology.

A simple TDD project¶

The project we are going to develop is available at https://github.com/lgiordani/simple_calculator.

This project is purposefully extremely simple. You don't need to be an experienced Python programmer to follow this chapter, but you need to know the basics of the language. The goal of this series of posts is not that of making you write the best Python code, but that of allowing you learn the TDD work flow, so don't be too worried if your code is not perfect.

Methodologies are like sports or arts: you cannot learn them just by reading their description on a book. You have to practice them. Thus, you should avoid as much as possible to just follow this chapter reading the code passively. Instead, you should try to write the code and to try new solutions to the problems that I discuss. This is very important, as it actually makes you use TDD. This way, at the end of the chapter you will have a personal experience of what TDD is like.

The repository is tagged, and at the end of each section you will find a link to the relative tag that contains my working solution. Please note that it is entirely possible your solution is different from mine: there are several aspects of coding, like for example style, that are not related to unit testing and TDD.

Setup the project¶

Clone the project repository and move to the branch develop. The branch master contains the full solution, and I use it to maintain the repository, but if you want to code along you need to start from scratch. I recommend you fork the repository on GitHub so that you are able to commit your changes.

git clone https://github.com/YOURUSERNAME/simple_calculator
cd simple_calculator
git checkout --track origin/develop

Create a virtual environment following your preferred process and install the requirements

pip install -r requirements/dev.txt

You should at this point be able to run

pytest -svv

and get an output like

================================ test session starts ===============================
platform XXXX -- Python XXXX, pytest-XXXX, py-XXXX, pluggy-XXXX -- XXXX
cachedir: .pytest_cache
rootdir: XXXX
configfile: XXXX
plugins: XXXX
collected 0 items 

=============================== no tests ran in 0.02s ==============================

You can see here the operating system and a short list of the versions of the main packages involved in running pytest: Python, pytest itself, and some of its components and plugins. You can also see here where pytest is reading its configuration from. As this header is standard I will omit it from the output that I will show in the rest of the chapter. The specific versions of the packages are not important for this series.

Requirements¶

The goal of the project is to write a class SimpleCalculator that performs calculations: addition, subtraction, multiplication, and division. Addition and multiplication shall accept multiple arguments. Division shall return a float value, and division by zero shall return the string "inf". Multiplication by zero must raise a ValueError exception. The class will also provide a function to compute the average of an iterable like a list. This function gets two optional upper and lower thresholds and should remove from the computation the values that fall outside these boundaries.

As you can see the requirements are pretty simple, and a couple of them are definitely not "good" requirements, like the behaviour of division and multiplication. I added those requirements for the sake of example, to show how to deal with exceptions when developing in TDD.

An interesting topic to discuss is that of data types: shall the calculator perform addition between integers or between floats? What about complex numbers, strings, and other items that can be "added" together? And what about the other operations? I consider this an advanced topic, in particular in Python, so for now I will consider only integers as inputs and discuss the problem of different types later in the series.

Step 1 - Adding two numbers¶

The first test we are going to write is one that checks if the class SimpleCalculator can perform an addition. Add the following code to the file tests/test_main.py

from simple_calculator.main import SimpleCalculator 1

def test_add_two_numbers(): 2
    calculator = SimpleCalculator()

    result = calculator.add(4, 5)

    assert result == 9

As you can see the first thing we do is to import the class SimpleCalculator 1 that we are supposed to write. This class doesn't exist yet, don't worry, you didn't skip any passage.

The test is a standard function 2 (this is how pytest works), and the function name shall begin with test_ so that pytest can automatically discover all the tests. I tend to give my tests a descriptive name, so it is easier later to come back and understand what the test is about with a quick glance. You are free to follow the style you prefer but in general remember that naming components in a proper way is one of the most difficult things in programming. So better to get a handle on it as soon as possible.

The body of the test function is pretty simple. The class SimpleCalculator is instantiated, and the method add of the instance is called with two numbers, 4 and 5. The result is stored in the variable result, which is later the subject of the test itself. The statement assert result == 9 first computes result == 9 which is a boolean, with a value that is either True or False. The keyword assert, then, silently passes if the argument is True, but raises an exception if it is False.

And this is how you write tests in pytest: if your code doesn't raise any exception the test passes, otherwise it fails. The keyword assert is used to force an exception in case of wrong result. Remember that pytest doesn't consider the return value of the function, so it can detect a failure only if it raises an exception.

Save the file and go back to the terminal. Execute pytest -svv and you should receive the following error message

====================================== ERRORS ======================================
_______________________ ERROR collecting tests/test_main.py _______________________

[...]

tests/test_main.py:4: in <module>
    from simple_calculator.main import SimpleCalculator
E   ImportError: cannot import name 'SimpleCalculator' from 'simple_calculator.main'
!!!!!!!!!!!!!!!!!!!!!! Interrupted: 1 errors during collection !!!!!!!!!!!!!!!!!!!!!
============================== 1 error in 0.20 seconds =============================

No surprise here, actually, as we just tried to use something that doesn't exist. This is good, the test is showing us that something we suppose exists actually doesn't.

This, by the way, is not yet an error in a test. The error happens very soon, during the tests collection phase (as shown by the message in the bottom line Interrupted: 1 errors during collection). Given this, the methodology is still valid, as we wrote a test and it fails because of an error or a missing feature in the code.

Let's fix this issue. Open the file simple_calculator/main.py and add this code

class SimpleCalculator:
    pass

But, I hear you scream, this class doesn't implement any of the requirements that are in the project. Yes, this is the hardest lesson you have to learn when you start using TDD. The development of the code is ruled by the tests, not by the requirements. The requirements are used to write the tests, the tests are used to write the code. You shouldn't worry about something that is more than one level above the current one in this workflow.

Run the test again, and this time you should receive a different error, that is

tests/test_main.py::test_add_two_numbers FAILED

===================================== FAILURES =====================================
______________________________ test_add_two_numbers _______________________________


    def test_add_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.add(4, 5)
E       AttributeError: 'SimpleCalculator' object has no attribute 'add'

tests/test_main.py:10: AttributeError
============================= 1 failed in 0.04 seconds =============================

This is the first proper pytest failure report that we receive. You see a list of files containing tests and the result of each test

tests/test_main.py::test_add_two_numbers FAILED

Later we will see that the syntax FILENAME::TESTNAME can be given directly to pytest to run a single test. In this case we already have only one test, but later you might run a single failing test giving the name shown here on the command line. For example

pytest -svv tests/test_main.py::test_add_two_numbers

The second part of the output shows details on the failing tests, if any

______________________________ test_add_two_numbers _______________________________

    def test_add_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.add(4, 5)
E       AttributeError: 'SimpleCalculator' object has no attribute 'add'

tests/test_main.py:10: AttributeError

For each failing test, pytest shows a header with the name of the test and the part of the code that raised the exception. At the end of each box, pytest shows the line of the test file where the error happened.

Back to the project. The new error is no surprise, as the test uses the method add that wasn't defined in the class. I bet you already guessed what I'm going to do, didn't you? This is the code that you should add to the class

class SimpleCalculator:
    def add(self):
        pass

And again, as you notice, we made the smallest possible addition to the code to pass the test. Running pytest again you should receive a different error message

_______________________________ test_add_two_numbers _______________________________

    def test_add_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.add(4, 5)
E       TypeError: add() takes 1 positional argument but 3 were given

tests/test_main.py:10: TypeError

The function we defined doesn't accept any argument other than self (def add(self)), but in the test we pass three of them (calculator.add(4, 5). Remember that in Python self is passed implicitly when you call a function. Our move at this point is to change the function to accept the parameters that it is supposed to receive, namely two numbers. The code now becomes

class SimpleCalculator:
    def add(self, a, b):
        pass

Run the test again, and you will receive another error

______________________________ test_add_two_numbers ________________________________

    def test_add_two_numbers():
        calculator = SimpleCalculator()

        result = calculator.add(4, 5)

>       assert result == 9
E       assert None == 9
E         -None
E         +9

tests/test_main.py:12: AssertionError

The function returns None, as it doesn't contain any code, while the test expects it to return 9. What do you think is the minimum code you can add to pass this test?

Well, the answer is

class SimpleCalculator:
    def add(self, a, b):
        return 9

and this may surprise you (it should!). You might have been tempted to add some code that performs an addition between a and b, but this would violate the TDD principles, because you would have been driven by the requirements and not by the tests.

When you run pytest again, you will be rewarded by a success message

tests/test_main.py::test_add_two_numbers PASSED

I know this sound weird, but think about it for a moment: if your code works (that is, it passes the tests), you don't need to change anything, as your tests should specify everything the code should do. Maybe in the future you will discover that this solution is not good enough, and at that point you will have to change it (this will happen with the next test, in this case). But for now everything works, and you shouldn't implement more than this.

Git tag: step-1-adding-two-numbers

Step 2 - Adding three numbers¶

The requirements state that "Addition and multiplication shall accept multiple arguments". This means that we should be able to execute not only add(4, 5) like we did, but also add(4, 5, 11), add(4, 5, 11, 2), and so on. We can start testing this behaviour with the following test, that you should put in tests/test_main.py, after the previous test that we wrote.

def test_add_three_numbers():
    calculator = SimpleCalculator()

    result = calculator.add(4, 5, 6)

    assert result == 15

This test fails when we run the test suite

_____________________________ test_add_three_numbers _______________________________

    def test_add_three_numbers():
        calculator = SimpleCalculator()

>       result = calculator.add(4, 5, 6)
E       TypeError: SimpleCalculator.add() takes 3 positional arguments but 4 were given

tests/test_main.py:18: TypeError

for the obvious reason that the function we wrote in the previous section accepts only 2 arguments other than self. What is the minimum code that you can write to fix this test?

Well, the simplest solution is to add another argument, so my first attempt is

class SimpleCalculator:
    def add(self, a, b, c):
        return 9

which solves the previous error, but creates a new one. If that wasn't enough, it also makes the first test fail!

______________________________ test_add_two_numbers ________________________________

    def test_add_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.add(4, 5)
E       TypeError: SimpleCalculator.add() missing 1 required positional argument: 'c'

tests/test_main.py:10: TypeError
_____________________________ test_add_three_numbers _______________________________

    def test_add_three_numbers():
        calculator = SimpleCalculator()

        result = calculator.add(4, 5, 6)

>       assert result == 15
E       assert 9 == 15

tests/test_main.py:20: AssertionError

The first test now fails because the new add method requires three arguments and we are passing only two. The second tests fails because the method add returns 9 and not 15 as expected by the test.

When multiple tests fail it's easy to feel discomforted and lost. Where are you supposed to start fixing this? Well, one possible solution is to undo the previous change and to try a different solution, but in general you should try to get to a situation in which only one test fails.

This is very important as it allows you to focus on one single test and thus one single problem. Clearly, we need to keep an eye on the global problem that we are trying to solve, but real test batteries can contain hundreds of tests and it is not practical to try to tackle all of them together.

Commenting tests to make them inactive is a perfectly valid way to have only one failing test. Pytest, however, has a smarter solution: you can use the option -k that allows you to specify a matching name. That option has a lot of expressive power, but for now we can just give it the name of the test that we want to run

pytest -svv -k test_add_two_numbers

This option allows you to select multiple tests that share the same prefix, for example. If you want to run a single specific test you can also name it on the command line with the syntax we discussed previously

pytest -svv tests/test_main.py::test_add_two_numbers

Either way, pytest will run only the first test and return the same result returned before, since we didn't change the test itself

______________________________ test_add_two_numbers ________________________________

    def test_add_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.add(4, 5)
E       TypeError: SimpleCalculator.add() missing 1 required positional argument: 'c'

tests/test_main.py:10: TypeError

To fix this error we can obviously revert the addition of the third argument, but this would mean going back to the previous solution. Obviously tests focus on a very small part of the code, but we have to keep in mind what we are doing in terms of the big picture. A better solution is to add a default value to the third argument. The additive identity is 0, so the new code of the method add is

class SimpleCalculator:
    def add(self, a, b, c=0):
        return 9

And this makes the first test pass. At this point we can run the full suite with pytest -svv and see what happens

_____________________________ test_add_three_numbers ______________________________

    def test_add_three_numbers():
        calculator = SimpleCalculator()

        result = calculator.add(4, 5, 6)

>       assert result == 15
E       assert 9 == 15

tests/test_main.py:20: AssertionError

The second test still fails, because the returned value that we hard coded doesn't match the expected one. At this point the tests show that our previous solution (return 9) is not sufficient anymore, and we have to try to implement something more complex.

I want to stress this. You should implement the minimal change in the code that makes tests pass. If that solution is not enough there will be a test that shows it. Now, as you can see, the addition of a new requirement changes the tests, adding a new one, and the old solution is not sufficient any more.

How can we solve this? We know that writing return 15 will make the first test fail (you may try, if you want), so here we have to be a bit smarter and try a better solution, that in this case is actually to implement a real sum

class SimpleCalculator:
    def add(self, a, b, c=0):
        return a + b + c

This solution makes both tests pass, so the entire suite runs without errors.

Git tag: step-2-adding-three-numbers

I can see your face, your are probably frowning at the fact that it took us 10 minutes to write a method that performs the addition of two or three numbers. On the one hand, keep in mind that I'm going at a very slow pace, this being an introduction, and for these first tests it is better to take the time to properly understand every single step. Later, when you will be used to TDD, some of these steps will be implicit. On the other hand, TDD is slower than untested development, but the time that you invest writing tests now is usually negligible compared to the amount of time you would spend trying to identify and fix bugs later.

Step 3 - Adding multiple numbers¶

The requirements are not yet satisfied, however, as they mention "multiple" numbers and not just three. How can we test that we can add a generic amount of numbers? We might add a test_add_four_numbers, a test_add_five_numbers, and so on, but this will cover specific cases and will never cover all of them. Sad to say, it is impossible to test that generic condition, or, at least in this case, so complex that it is not worth trying to do it.

What you shall do in TDD is to test boundary cases. In general you should always try to find the so-called "corner cases" of your algorithm and write tests that show that the code covers them. For example, if you are testing some code that accepts as inputs a number from 1 to 100, you need a test that runs it with a generic number like 42 (which is far from being generic, but don't panic!), but you definitely want to have a specific test that runs the algorithm with the number 1 and one that runs with the number 100. You also want to have tests that show the algorithm doesn't work with 0 and with 101, but we will talk later about testing error conditions.

In our example there is no real limitation to the number of arguments that you pass to your function. Before Python 3.7 there was a limit of 256 arguments, which has been removed in that version of the language, but these are limitations enforced by an external system, and they are not real boundaries of your algorithm.

The definition of "external system" obviously depends on what you are testing. If you are implementing a programming language you want to have tests that show how many arguments you can pass to a function, or that check the amount of memory used by certain language features. In this case we accept the Python language as the environment in which we work, so we don't want to test its features.

The solution, in this case, might be to test a reasonable high amount of input arguments, to check that everything works. In particular, we should try to keep in mind that our goal is to devise as much as possible a generic solution. For example, we easily realise that we cannot come up with a function like

    def add(self, a, b, c=0, d=0, e=0, f=0, g=0, h=0, i=0):

as it is not generic, it is just covering a greater amount of inputs (9, in this case, but not 10 or more).

That said, a good test might be the following

def test_add_many_numbers():
    numbers = range(100)

    calculator = SimpleCalculator()

    result = calculator.add(*numbers)

    assert result == 4950

which creates an array (strictly speaking a range, which is an iterable) of all the numbers from 0 to 99. The sum of all those numbers is 4950, which is what the algorithm shall return.

Please note that the assertion doesn't implement any algorithm to find the solution. I calculated the answer manually and hard coded it in the test. You should try as much as possible to minimise the algorithmic complexity of tests, instead "stating the facts". The reason is simple: the more complex the code of the test is, the higher the chances of introducing a bug in the test.

The test suite fails because we are giving the function too many arguments

______________________________ test_add_many_numbers _______________________________

    def test_add_many_numbers():
        numbers = range(100)

        calculator = SimpleCalculator()

>       result = calculator.add(*numbers)
E       TypeError: SimpleCalculator.add() takes from 3 to 4 positional arguments but 101 were given

tests/test_main.py:28: TypeError

The minimum amount of code that we can add, this time, will not be so trivial, as we have to pass three tests. This is actually the greatest advantage of TDD: the tests that we wrote are still there and will check that the previous conditions are still satisfied. And since tests are committed with the code they will always be there.

The Python way to support a generic number of arguments (technically called variadic functions) is through the use of the syntax *args, which stores in args a tuple that contains all the arguments.

class SimpleCalculator:
    def add(self, *args):
        return sum(args)

At that point we can use the built-in function sum to sum all the arguments. This solution makes the whole test suite pass without errors, so it is correct.

Git tag: step-3-adding-multiple-numbers

Pay attention here, please. In TDD, a solution is not correct when it is beautiful, when it is smart, or when it uses the latest feature of the language. All these things are good, but TDD wants your code to pass the tests. So, your code might be ugly, convoluted, and slow, but if it passes the test it is correct. This in turn means that TDD doesn't cover all the needs of your software project. Delivering fast routines, for example, might be part of the advantage you have on your competitors, but it is not really testable with the TDD methodology (typically, performance testing is done in a completely different way).

Part of the TDD methodology, then, deals with "refactoring", which means changing the code in a way that doesn't change the outputs, which in turns means that all your tests keep passing. Once you have a proper test suite in place, you can focus on the beauty of the code, or you can introduce smart solutions according to what the language allows you to do. We will discuss refactoring further later in this post.

Step 4 - Subtraction¶

From the requirements we know that we have to implement a function to subtract numbers, but this doesn't mention multiple arguments (as it would be complex to define what subtracting 3 of more numbers actually means). The tests that implements this requirements is

def test_subtract_two_numbers():
    calculator = SimpleCalculator()

    result = calculator.sub(10, 3)

    assert result == 7

which doesn't pass with the following error

____________________________ test_subtract_two_numbers ____________________________

    def test_subtract_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.sub(10, 3)
E       AttributeError: 'SimpleCalculator' object has no attribute 'sub'

tests/test_main.py:36: AttributeError

Now that you understood the TDD process, and that you know you should avoid over-engineering, you can also skip some of the passages that we run through in the previous sections. A good solution for this test is

    def sub(self, a, b):
        return a - b

which makes the test suite pass.

Git tag: step-4-subtraction

Step 5 - Multiplication¶

It's time to move to multiplication, which has many similarities to addition. The requirements state that we have to provide a function to multiply numbers and that this function shall allow us to multiply multiple arguments. In TDD you should try to tackle problems one by one, possibly dividing a bigger requirement in multiple smaller ones.

In this case the first test can be the multiplication of two numbers, as it was for addition.

def test_mul_two_numbers():
    calculator = SimpleCalculator()

    result = calculator.mul(6, 4)

    assert result == 24

And the test suite fails as expected with the following error

______________________________ test_mul_two_numbers _______________________________

    def test_mul_two_numbers():
        calculator = SimpleCalculator()

>       result = calculator.mul(6, 4)
E       AttributeError: 'SimpleCalculator' object has no attribute 'mul'

tests/test_main.py:44: AttributeError

We face now a classical TDD dilemma. Shall we implement the solution to this test as a function that multiplies two numbers, knowing that the next test will invalidate it, or shall we already consider that the target is that of implementing a variadic function and thus use *args directly?

In this case the choice is not really important, as we are dealing with very simple functions. In other cases, however, it might be worth recognising that we are facing the same issue we solved in a similar case and try to implement a smarter solution from the very beginning. In general, however, you should not implement anything that you don't plan to test in one of the next few tests that you will write.

If we decide to follow the strict TDD, that is implement the simplest first solution, the bare minimum code that passes the test would be

    def mul(self, a, b):
        return a * b

Git tag: step-5-multiply-two-numbers

To show you how to deal with redundant tests I will in this case choose the second path, and implement a smarter solution for the present test. Keep in mind however that it is perfectly correct to implement that solution shown above and then move on and try to solve the problem of multiple arguments later.

The problem of multiplying a tuple of numbers can be solved in Python using the function reduce. This function implements a typical algorithm that "reduces" an array to a single number, applying a given function. The algorithm steps are the following

1. Apply the function to the first two elements 2. Remove the first two elements from the array 3. Apply the function to the result of the previous step and to the first element of the array 4. Remove the first element 5. If there are still elements in the array go back to step 3

So, suppose the function is

def mul2(a, b):
    return a * b

and the array is

a = [2, 6, 4, 8, 3]

The steps followed by the algorithm will be

1. Apply the function to 2 and 6 (first two elements). The result is 2 * 6, that is 12 2. Remove the first two elements, the array is now a = [4, 8, 3] 3. Apply the function to 12 (result of the previous step) and 4 (first element of the array). The new result is 12 * 4, that is 48 4. Remove the first element, the array is now a = [8, 3] 5. Apply the function to 48 (result of the previous step) and 8 (first element of the array). The new result is 48 * 8, that is 384 6. Remove the first element, the array is now a = [3] 7. Apply the function to 384 (result of the previous step) and 3 (first element of the array). The new result is 384 * 3, that is 1152 8. Remove the first element, the array is now empty and the procedure ends

Going back to our class SimpleCalculator, we might import reduce from the module functools and use it on the array args. We need to provide a function that we can define in the function mul itself.

from functools import reduce


class SimpleCalculator:
    [...]

    def mul(self, *args):
        def mul2(a, b):
            return a * b

        return reduce(mul2, args)

Git tag: step-5-multiply-two-numbers-smart

More information about the algorithm reduce can be found on the MapReduce Wikipedia page https://en.wikipedia.org/wiki/MapReduce. The Python function documentation can be found at https://docs.python.org/3.10/library/functools.html#functools.reduce.

The above code makes the test suite pass, so we can move on and address the next problem. As happened with addition we cannot properly test that the function accepts a potentially infinite number of arguments, so we can test a reasonably high number of inputs.

def test_mul_many_numbers():
    numbers = range(1, 10)

    calculator = SimpleCalculator()

    result = calculator.mul(*numbers)

    assert result == 362880

Git tag: step-5-multiply-many-numbers

We might use 100 arguments as we did with addition, but the multiplication of all numbers from 1 to 100 gives a result with 156 digits and I don't really need to clutter the tests file with such a monstrosity. As I said, testing multiple arguments is testing a boundary, and the idea is that if the algorithm works for 2 numbers and for 10 it will work for 10 thousands arguments as well.

If we run the test suite now all tests pass, and this should worry you.

Yes, you shouldn't be happy. When you follow TDD each new test that you add should fail. If it doesn't fail you should ask yourself if it is worth adding that test or not. This is because chances are that you are adding a useless test and we don't want to add useless code, because code has to be maintained, so the less the better.

In this case, however, we know why the test already passes. We implemented a smarter algorithm as a solution for the first test knowing that we would end up trying to solve a more generic problem. And the value of this new test is that it shows that multiple arguments can be used, while the first test doesn't.

So, after these considerations, we can be happy that the second test already passes.

Step 6 - Refactoring¶

Previously, I introduced the concept of refactoring, which means changing the code without altering the results. How can you be sure you are not altering the behaviour of your code? Well, this is what the tests are for. If the new code keeps passing the test suite you can be sure that you didn't remove any feature.

In theory, refactoring shouldn't add any new behaviour to the code, as it should be an idempotent transformation. There is no real practical way to check this, and we will not bother with it now. You should be concerned with this if you are discussing security, as your code shouldn't add any entry point you don't want to be there. In this case you will need tests that check the absence of features instead of their presence.

This means that if you have no tests you shouldn't refactor. But, after all, if you have no tests you shouldn't have any code, either, so refactoring shouldn't be a problem you have. If you have some code without tests (I know you have it, I do), you should seriously consider writing tests for it, at least before changing it. More on this in a later section.

For the time being, let's see if we can work on the code of the class SimpleCalculator without altering the results. I do not really like the definition of the function mul2 inside the function mul. It is obviously perfectly fine and valid, but for the sake of example I will pretend we have to get rid of it.

Python provides a useful function to multiply two objects in the module operator of the standard library

import operator
from functools import reduce


class SimpleCalculator:
    [...]

    def mul(self, *args):
        return reduce(operator.mul, args)

Running the test suite I can see that all the test pass, so my refactoring is correct.

Git tag: step-6-refactoring

Final words¶

Well, I think we learned a lot. We started with no knowledge of TDD and we managed to implement a fully tested class with 3 methods. We also briefly touched the topic of refactoring, which is of paramount importance in development. In the next post I will cover the remaining requirements: division, testing exceptions, and the average function.

Updates¶

2021-01-03: George fixed a typo, thanks!

2021-08-11: Andrea Mignone fixed a link. Thank you!

2023-09-03: Dmitry Labazkin and Ilaletdinov Almaz suggested using operator.mul instead of a lambda in the final refactoring. Thanks both!

Feedback¶

Feel free to reach me on Twitter if you have questions. The GitHub issues page is the best place to submit corrections.