Book Image

Crafting Test-Driven Software with Python

By : Alessandro Molina
Book Image

Crafting Test-Driven Software with Python

By: Alessandro Molina

Overview of this book

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.
Table of Contents (18 chapters)
1
Section 1: Software Testing and Test-Driven Development
6
Section 2: PyTest for Python Testing
13
Section 3: Testing for the Web
16
About Packt

Running subsets of the testsuite

In the previous chapters, we saw how to divide our test suite into subsets that we can run on demand based on their purpose and cost. The way to do so involved dividing the tests by directory or by name, such that we could point the test runner to a specific directory or filter for test names with the -k option.

While those strategies are available on pytest too, pytest provides more ways to organize and divide tests; one of them being markers.

Instead of naming all our smoke tests "test_smoke_something", for example, we could just name the test "test_something" and mark it as a smoke test. Or, we could mark slow tests, so that we can avoid running slow ones during the most frequent runs.

Marking a test is as easy as decorating it with @pytest.mark.marker, where marker is our custom label. For example, we could create two tests and use @pytest.mark.first to mark the first of the two tests:

import pytest


@pytest.mark.first
def test_one...