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Crafting Test-Driven Software with Python
Crafting Test-Driven Software with Python

Crafting Test-Driven Software with Python: Write test suites that scale with your applications' needs and complexity using Python and PyTest

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Crafting Test-Driven Software with Python

Getting Started with Software Testing

Many think that the big step from "coding" to "software engineering" is made by having elegant architectures, well-defined execution plans, and software that moves big companies' processes. This mostly comes from our vision of the classic industrial product development world, where planning mostly mattered more than execution, because the execution was moved forward by an assembly line and software was an expensive internal utility that only big companies could afford

As software development science moved forward and matured, it became clear that classic industrial best practices weren't always a great fit for it. The reason being that every software product was very different, due to the technologies involved, the speed at which those technologies evolve, and in the end the fact that different software had to do totally different things. Thus the idea developed that software development was more similar to craftsmanship than to industry.

If you embrace that it's very hard, and not very effective, to try to eliminate uncertainty and issues with tons of preparation work due to the very nature of software itself, it becomes evident that the most important part of software development is detecting defects and ensuring it achieves the expected goals. Those two things are usually mostly done by having tests and a fitness function that can verify the software does what we really mean it to – founding pieces of the whole Software Quality Control discipline, which is what this chapter will introduce and, in practice, what this book is all about.

In this chapter, we will go through testing software products and the best practices in quality control. We will also introduce automatic tests and how they are superseding manual testing. We will take a look at what Test-Driven Development (TDD) is and how to apply it in Python, giving some guidance on how to distinguish between the various categories of tests, how to implement them, and how to get the right balance between test efficacy and test cost.

In this chapter, we will cover the following:

  • Introducing software testing and quality control
  • Introducing automatic tests and test suites
  • Introducing test-driven development and unit tests
  • Understanding integration and functional tests
  • Understanding the testing pyramid and trophy

Technical requirements

Introducing software testing and quality control

From the early days, it was clear that like any other machine, software needed a way to verify it was working properly and was built with no defects.

Software development processes have been heavily inspired by manufacturing industry standards, and early on, testing and quality control were introduced into the product development life cycle. So software companies frequently have a quality assurance team that focuses on setting up processes to guarantee robust software and track results.

Those processes usually include a quality control process where the quality of the built artifact is assessed before it can be considered ready for users.

The quality control process usually achieves such confidence through the execution of a test plan. This is usually a checklist that a dedicated team goes through during the various phases of production to ensure the software behaves as expected.

Test plans

A test plan is composed of multiple test cases, each specifying the following:

  • Preconditions: What's necessary to be able to verify the case
  • Steps: Actions that have to succeed when executed in the specified order
  • Postconditions: In which state the system is expected to be at the end of the steps

A sample test case of software where logging in with a username and password is involved, and we might want to allow the user to reset those, might look like the following table:

Test Case: 2.2 - Change User Password

Preconditions:

  • A user, user1 exists
  • The user is logged in as user1
  • The user is at the main menu
# Action Expected Response Success / Fail
1 Click the change password button. The system shows a dialog to insert a new password.
2

Enter newpass.

The dialog shows 7 asterisks in the password field.
3 Click the OK button. The system shows a dialog with a success message.
4 Wait 2 seconds. The success dialog goes away.

Postconditions:

  • The user1 password is now newpass

These test cases are divided into cases, are manually verified by a dedicated team, and a sample of them is usually selected to be executed during development, but most of them are checked when the development team declared the work done.

This meant that once the team finishes its work, it takes days/weeks for the release to happen, as the whole software has to be verified by humans clicking buttons, with all the unpredictable results that involves, as humans can get distracted, pressing the wrong button or receiving phone calls in the middle of a test case.

As software usage became more widespread, and business-to-consumer products became the norm, consumers started to appreciate faster release cycles. Companies that updated their products with new features frequently were those that ended up dominating the market in the long term.

If you think about modern release cycles, we are now used to getting a new version of our favorite mobile application weekly. Such applications are probably so complex that they involve thousands of test cases. If all those cases had to be performed by a human, there would be no way for the company to provide you with frequent releases.

The worst thing you can do, by the way, is to release a broken product. Your users will lose confidence and will switch to other more reliable competitors if they can't get their job done due to crashes or bugs. So how can we deliver such frequent releases without reducing our test coverage and thus incurring more bugs?

The solution came from automating the test process. So while we learned how to detect defects by writing and executing test plans, it's only by making them automatic that we can scale them to the number of cases that will ensure robust software in the long term.

Instead of having humans test software, have some other software test it. What a person does in seconds can happen in milliseconds with software and you can run thousands of tests in a few minutes.

Introducing automatic tests and test suites

Automated testing is, in practice, the art of writing another piece of software to test an original piece of software.

As testing a whole piece of software has to take millions of variables and possible code paths into account, a single program trying to test another one would be very complex and hard to maintain. For this reason, it's usually convenient to split that program into smaller isolated programs, each being a test case.

Each test case contains all the instructions that are required to set up the target software in a state where the parts that are the test case areas of interest can be tested, the tests can be done, and all the conditions can be verified and reset back to the state of the target software so a subsequent test case can find a known state from which to start.

When using the unittest module that comes with the Python Standard Library, each test case is declared by subclassing from the unittest.TestCase class and adding a method whose name starts with test, which will contain the test itself:

import unittest

class MyTestCase(unittest.TestCase):
def test_one(self):
pass

Trying to run our previous test will do nothing by the way:

$ python 01_automatictests.py
$

We declared our test case, but we have nothing that runs it.

As for manually executed tests, the automatic tests need someone in charge of gathering all test cases and running them all. That's the role of a test runner.

Test runners usually involve a discovery phase (during which they detect all test cases) and a run phase (during which they run the discovered tests).

The unittest module provides all the components necessary to build a test runner that does both the discovery and execution of tests. For convenience, it even provides the unittest.main() method, which configures a test runner that, by default, will run the tests in the current module:

import unittest

class MyTestCase(unittest.TestCase):
def test_one(self):
pass

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

By adding a call to unittest.main() at the end of our tests, Python will automatically execute our tests when the module is invoked:

$ python 01_automatictests.py
.
----------------------------------------------------------------------
Ran 1 test in 0.000s

OK

We can confirm that the test we cared about was executed by using the -v option to print a more verbose output:

$ python 01_automatictests.py -v
test_one (__main__.MyTestCase) ... ok

----------------------------------------------------------------------
Ran 1 test in 0.000s

OK

During the discovery phase, unittest.main will look for all classes that inherit from unittest.TestCase within the module that is recognized as the main Python module (sys.modules['__main__']), and all those subclasses will be registered as test cases for the runner.

Individual tests are then defined by having methods with names starting with test in the test case classes. This means that if we add more methods with names that don't start with test, they won't be treated as tests:

class MyTestCase(unittest.TestCase):
def test_one(self):
pass

def notatest(self):
pass

Trying to start the test runner again will continue to run only the test_one test:

$ python 01_automatictests.py -v
test_one (__main__.MyTestCase) ... ok

----------------------------------------------------------------------
Ran 1 test in 0.000s

OK

In the previous example, only the test_one method was executed as a test, while notatest was recognized as not being a test but instead as a method that we are going to use ourselves in tests.

Being able to distinguish between tests (methods whose names start with test_) and other methods allows us to create helpers and utility methods within our test cases that the individual tests can reuse.

Given that a test suite is a collection of multiple test cases, to grow our test suite, we need to be able to actually write more than one single TestCase subclass and run its tests.

Multiple test cases

We already know that unittest.main is the function in charge of executing our test suite, but how can we make it execute more than one TestCase?

The discovery phase of unittest.main (the phase during which unittest.main decides which tests to run) looks for all subclasses or unittest.TestCase.

The same way we had MyTestCase tests executed, adding more test cases is as simple as declaring more classes:

import unittest


class MyTestCase(unittest.TestCase):
def test_one(self):
pass

def notatest(self):
pass


class MySecondTestCase(unittest.TestCase):
def test_two(self):
pass


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

Running the 01_automatictests.py module again will lead to both test cases being verified:

$ python 01_automatictests.py -v
test_two (__main__.MySecondTestCase) ... ok
test_one (__main__.MyTestCase) ... ok

----------------------------------------------------------------------
Ran 2 tests in 0.000s

OK

If a test case is particularly complex, it can even be divided into multiple individual tests, each checking a specific subpart of it:

class MySecondTestCase(unittest.TestCase):
def test_two(self):
pass

def test_two_part2(self):
pass

This allows us to divide the test cases into smaller pieces and eventually share setup and teardown code between the individual tests. The individual tests will be executed by the test runner in alphabetical order, so in this case, test_two will be executed before test_two_part2:

$ python 01_automatictests.py -v
test_two (__main__.MySecondTestCase) ... ok
test_two_part2 (__main__.MySecondTestCase) ... ok
test_one (__main__.MyTestCase) ... ok

In that run of the tests, we can see that MySecondTestCase was actually executed before MyTestCase because "MyS" is less than "MyT".

In any case, generally, it's a good idea to consider your tests as being executed in a random order and to not rely on any specific sequence of execution, because other developers might add more test cases, add more individual tests to a case, or rename classes, and you want to allow those changes with no additional issues. Especially since relying on a specific known execution order of your tests might limit your ability to parallelize your test suite and run test cases concurrently, which will be required as the size of your test suite grows.

Once more tests are added, adding them all into the same class or file quickly gets confusing, so it's usually a good idea to start organizing tests.

Organizing tests

If you have more than a few tests, it's generally a good idea to group your test cases into multiple modules and create a tests directory where you can gather the whole test plan:

├── 02_tests
│ ├── tests_div.py
│ └── tests_sum.py

Those tests can be executed through the unittest discover mode, which will look for all modules with names matching test*.py within a target directory and will run all the contained test cases:

$ python -m unittest discover 02_tests -v
test_div0 (tests_div.TestDiv) ... ok
test_div1 (tests_div.TestDiv) ... ok
test_sum0 (tests_sum.TestSum) ... ok
test_sum1 (tests_sum.TestSum) ... ok

----------------------------------------------------------------------
Ran 4 tests in 0.000s

OK

You can even pick which tests to run by filtering them with a substring with the -k parameter; for example, -k sum will only run tests that contain "sum" in their names:

$ python -m unittest discover 02_tests -k sum -v
test_sum0 (tests_sum.TestSum) ... ok
test_sum1 (tests_sum.TestSum) ... ok

----------------------------------------------------------------------
Ran 2 tests in 0.000s

OK

And yes, you can nest tests further as long as you use Python packages:

├── 02_tests
│ ├── tests_div
│ │ ├── __init__.py
│ │ └── tests_div.py
│ └── tests_sum.py

Running tests structured like the previous directory tree will properly navigate into the subfolders and spot the nested tests.

So running unittest in discovery mode over that direction will properly find the TestDiv and TestSum classes declared inside the files even when they are nested in subdirectories:

$ python -m unittest discover 02_tests -v
test_div0 (tests_div.tests_div.TestDiv) ... ok
test_div1 (tests_div.tests_div.TestDiv) ... ok
test_sum0 (tests_sum.TestSum) ... ok
test_sum1 (tests_sum.TestSum) ... ok

----------------------------------------------------------------------
Ran 4 tests in 0.000s

OK

Now that we know how to write tests, run them, and organize multiple tests in a test suite. We can start introducing the concept of TDD and how unit tests allow us to achieve it.

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Key benefits

  • Build robust Python applications using TDD and BDD methodologies
  • Test Python web applications using WebTest and web frameworks
  • Leverage PyTest to implement stringent testing mechanisms to ensure fault-tolerant applications

Description

Test-driven development (TDD) is a set of best practices that helps developers to build more scalable software and is used to increase the robustness of software by using automatic tests. This book shows you how to apply TDD practices effectively in Python projects. You’ll begin by learning about built-in unit tests and Mocks before covering rich frameworks like PyTest and web-based libraries such as WebTest and Robot Framework, discovering how Python allows you to embrace all modern testing practices with ease. Moving on, you’ll find out how to design tests and balance them with new feature development and learn how to create a complete test suite with PyTest. The book helps you adopt a hands-on approach to implementing TDD and associated methodologies that will have you up and running and make you more productive in no time. With the help of step-by-step explanations of essential concepts and practical examples, you’ll explore automatic tests and TDD best practices and get to grips with the methodologies and tools available in Python for creating effective and robust applications. By the end of this Python book, you will be able to write reliable test suites in Python to ensure the long-term resilience of your application using the range of libraries offered by Python for testing and development.

Who is this book for?

This book is for Python developers looking to get started with test-driven development and developers who want to learn about the testing tools available in Python. Developers who want to create web applications with Python and plan to implement TDD methodology with PyTest will find this book useful. Basic knowledge of Python programming is required.

What you will learn

  • Find out how tests can make your life easier as a developer and discover related best practices
  • Explore PyTest, the most widespread testing framework for Python
  • Get to grips with the most common PyTest plugins, including coverage, flaky, xdist, and picked
  • Write functional tests for WSGI web applications with WebTest
  • Run end-to-end tests for web applications using Robot Framework
  • Understand what test-driven development means and why it is important
  • Discover how to use the range of tools available in Python
  • Build reliable and robust applications
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Table of Contents

17 Chapters
Section 1: Software Testing and Test-Driven Development Chevron down icon Chevron up icon
Getting Started with Software Testing Chevron down icon Chevron up icon
Test Doubles with a Chat Application Chevron down icon Chevron up icon
Test-Driven Development while Creating a TODO List Chevron down icon Chevron up icon
Scaling the Test Suite Chevron down icon Chevron up icon
Section 2: PyTest for Python Testing Chevron down icon Chevron up icon
Introduction to PyTest Chevron down icon Chevron up icon
Dynamic and Parametric Tests and Fixtures Chevron down icon Chevron up icon
Fitness Function with a Contact Book Application Chevron down icon Chevron up icon
PyTest Essential Plugins Chevron down icon Chevron up icon
Managing Test Environments with Tox Chevron down icon Chevron up icon
Testing Documentation and Property-Based Testing Chevron down icon Chevron up icon
Section 3: Testing for the Web Chevron down icon Chevron up icon
Testing for the Web: WSGI versus HTTP Chevron down icon Chevron up icon
End-to-End Testing with the Robot Framework Chevron down icon Chevron up icon
About Packt Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
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Vit Apr 09, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great reference for those who want to learn more about testing and test-driven development practices. The book covers a wide variety of tools and concepts that are most commonly used in the software testing community. It offers a useful practical introduction to Python native unittest module and the pytest framework for those know how to test software in other languages but are just turning to Python and thus don't yet know which tools are available to them. Overall, highly recommended !
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Vincent Russo Apr 05, 2021
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The text starts off with a well-written overview of TDD (test-driven-development). Lots of code samples and examples that readers can use to more deeply understand some of the testing tools and concepts that the author covers. This is a practical text for a QA software developer who works primarily in Python and wants to improve their workflow by using some of the more modern offerings of various Python modules like `pytest`. Highly recommended!
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isela gradilla May 03, 2021
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This book is beginner friendly, as someone very comfortable with python I found this book very helpful as it is very detailed and well organized. I would highly recommend this if you are a python developer.
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Marco Carnini Apr 21, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book should be thought as a course in software testing. While the main focus is on test-driven development (starting from unit testing to go with integration and function testing), the book is way more than an abstract, arid and theoretical exposition of concepts and theory.The author present concrete examples of Test Driven Development (although idealized for the sake of shortness), and show how to build it up from scratch a test suite for the software. Different frameworks were illustrated (for example, I currently only use pytest) for different kind of projects (robot, tox, WebTest). Integration with travis is also presented (and much appreciated).I particularly like the exposition of behavior-driven development , which is a topic I always desire to learn. I am not an expert after only reading the book, but I feel the material is enough to start experimenting.In short: the book is detailed, it is easy to follow as long as an effort is paid in replicating the steps illustrated and represents a solid foundation to start practicing.A very good job.
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Vlad Bezden Mar 29, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a second book by Alessandro and as with the first book he did a great job. In this book, he covers all aspects of testing. Not only he provides how to test using different frameworks like unittest and pytest, but he also provides information about different strategies of testing, such as unit tests, TDD, BDD, ATDD. The one chapter that I like the most is "Test Doubles with a Chat Application" where he is talking about dependencies injection.This book everything you need to know about software testing.
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Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela