pytest

Test-Driving TestProject’s New Python SDK

TestProject recently released its new OpenSDK, and one of its major features is the inclusion of Python testing support! Since I love using Python for test automation, I couldn’t wait to give it a try. This article is my crash-course tutorial on writing Web UI tests in Python with TestProject.

What is TestProject?

TestProject is a free end-to-end test automation platform for Web, mobile, and API tests. It provides a cloud-based way to teams to build, run, share, and analyze tests. Manual testers can visually build tests for desktop or mobile sites using TestProject’s in-browser recorder and test builder. Automation engineers can use TestProject’s SDKs in Java, C#, and now Python for developing coded test automation solutions, and they can use packages already developed by others in the community through TestProject’s add-ons. Whether manual or automated, TestProject displays all test results in a sleek reporting dashboard with helpful analytics. And all of these features are legitimately free – there’s no tiered model or service plan.

Recently, TestProject announced the official release of its new OpenSDK. This new SDK (“software development kit”) provides a simple, unified interface for running tests with TestProject across multiple platforms and languages (now including Python). Things look exciting for the future of TestProject!

What’s My Interest?

It’s no secret that I love testing with Python. When I heard that TestProject added Python support, I knew I had to give it a try. I never used TestProject before, but I was interested to learn what it could do. Specifically, I wanted to see the value it could bring to reporting automated tests. In the Python space, test automation is slick, but reporting can be rough since frameworks like pytest and unittest are command-line-focused. I also wanted to see if TestProject’s SDK would genuinely help me automate tests or if it would get it my way. Furthermore, I know some great people in the TestProject community, so I figured it was time to jump in myself!

The Python SDK

TestProject’s Python SDK is an open-source project. It was originally developed by Bas Dijkstra, with the support of the TestProject team, and its code is hosted on GitHub. The Python SDK supports Selenium for Web UI automation (which will be the focus for this tutorial) and Appium for Android and iOS UI automation as well!

Since I’d never used TestProject before, let alone this new Python SDK, I wanted to review the code to see how to use it. Thankfully, the README included lots of helpful information and example code. When I looked at the code for TestProject’s BaseDriver, I discovered that it simply extend’s Selenium WebDriver’s RemoteDriver class. That means all the TestProject WebDrivers use exactly the same API as Python’s Selenium WebDriver implementation. To me, that was a big relief. I know WebDriver’s API very well, so I wouldn’t need to learn anything different in order to use TestProject. It also means that any test automation project can be easily retrofitted to use TestProject’s SDKs – they just need to swap in a new WebDriver object!

Setup Steps

TestProject has a straightforward architecture. Users sign up for free TestProject accounts online. Then, they set up their own machines for running tests. Each testing machine must have the TestProject agent installed and linked to a user’s account. When tests run, agents automatically push results to the TestProject cloud. Users can then log into the TestProject portal to view and analyze results. They can invite team mates to share results, and they can also set up multiple test machines with agents. Users can even integrate TestProject with other tools like Jenkins, qTest, and Sauce Labs. The TestProject docs, especially the ecosystem diagram, explain everything in more detail.

When I did my test drive, I created a TestProject account, installed the agent on my Mac, and ran Python Web UI tests from my Mac. I already had the latest version of Python installed (Python 3.8 at the time of writing this article). I also already had my target browsers installed: Google Chrome and Mozilla Firefox.

Below are the precise steps I followed to set up TestProject:

1. Sign up for an account

TestProject accounts are “free forever.” Use this signup link.

The TestProject signup page

2. Download the TestProject Agent

The signup wizard should direct you to download the TestProject agent. If not, you can always download it from the TestProject dashboard. Be warned, the download package is pretty large – the macOS package was 345 MB. Alternatively, you can fetch the agent as a container image from Docker Hub.

The TestProject agent download page

The TestProject agent contains all the stuff needed to run tests and upload results to the TestProject app in the cloud. You don’t need to install WebDriver executables like ChromeDriver or geckodriver. Once the agent is downloaded, install it on the machine and register the agent with your account. For me, registration happened automatically.

This is what the TestProject agent looks like when running on macOS. You can also close this window to let it run in the background.

3. Find your developer token

You’ll need to use your developer token to connect your automated tests to your account in the TestProject app. The signup wizard should reveal it to you, but you can always find it (and also reset it) on the Integrations page.

The Integrations page. Check here for your developer token. No, you can’t use mine.

4. Install the Python SDK

TestProject’s Python SDK is distributed as a package through PyPI. To install it, simply run pip install testproject-python-sdk at the command line. This package will also install dependencies like selenium and requests.

A Classic Web UI Test

After setting up my Mac to use TestProject, it was time to write some Web UI tests in Python! Since I discovered that TestProject’s WebDriver objects could easily retrofit any existing test automation project, I thought, “What if I try to run my PyCon 2020 tutorial project with TestProject?” For PyCon 2020, I gave an online tutorial about building a Web UI test automation project in Python from the ground up using pytest and Selenium WebDriver. The tutorial includes one test case: a DuckDuckGo web search and verification. I thought it would be easy to integrate with TestProject since I already had the code. Thankfully, it was!

Below, I’ll walk though my code. You can check out my example project repository from GitHub at AndyLPK247/testproject-python-sdk-example. My code will be a bit more advanced than the examples shown in the Python SDK’s README or in Bas Dijkstra’s tutorial article because it uses the Page Object Model and pytest fixtures. Make sure to pip install pytest, too.

1. Write the test steps

The test case covers a simple DuckDuckGo web search. Whenever I automate tests, I always write out the steps in plain language. Good tests follow the Arrange-Act-Assert pattern, and I like to use Gherkin’s Given-When-Then phrasing. Here’s the test case:

Scenario: Basic DuckDuckGo Web Search
    Given the DuckDuckGo home page is displayed
    When the user searches for "panda"
    Then the search result query is "panda"
    And the search result links pertain to "panda"
    And the search result title contains "panda"

2. Specify automation inputs

Inputs configure how automated tests run. They can be passed into a test automation solution using configuration files. Testers can then easily change input values in the config file without changing code. Automation should read config files once at the start of testing and inject necessary inputs into every test case.

In Python, I like to use JSON for config files. JSON data is simple and hierarchical, and Python includes a module in its standard library named json that can parse a JSON file into a Python dictionary in one line. I also like to put config files either in the project root directory or in the tests directory.

Here’s the contents of config.json for this test:

{
  "browser": "Chrome",
  "implicit_wait": 10,
  "testproject_projectname": "TestProject Python SDK Example",
  "testproject_token": ""
}
  • browser is the name of the browser to test
  • implicit_wait is the implicit waiting timeout for the WebDriver instance
  • testproject_projectname is the project name to use for this test suite in the TestProject app
  • testproject_token is the developer token

3. Read automation inputs

Automation code should read inputs one time before any tests run and then inject inputs into appropriate tests. pytest fixtures make this easy to do.

I created a fixture named config in the tests/conftest.py module to read config.json:

import json
import pytest


@pytest.fixture
def config(scope='session'):

  # Read the file
  with open('config.json') as config_file:
    config = json.load(config_file)
  
  # Assert values are acceptable
  assert config['browser'] in ['Firefox', 'Chrome', 'Headless Chrome']
  assert isinstance(config['implicit_wait'], int)
  assert config['implicit_wait'] > 0
  assert config['testproject_projectname'] != ""
  assert config['testproject_token'] != ""

  # Return config so it can be used
  return config

Setting the fixture’s scope to “session” means that it will run only one time for the whole test suite. The fixture reads the JSON config file, parses its text into a Python dictionary, and performs basic input validation. Note that Firefox, Chrome, and Headless Chrome will be supported browsers.

4. Set up WebDriver

Each Web UI test should have its own WebDriver instance so that it remains independent from other tests. Once again, pytest fixtures make setup easy.

The browser fixture in tests/conftest.py initialize the appropriate TestProject WebDriver type based on inputs returned by the config fixture:

from selenium.webdriver import ChromeOptions
from src.testproject.sdk.drivers import webdriver


@pytest.fixture
def browser(config):

  # Initialize shared arguments
  kwargs = {
    'projectname': config['testproject_projectname'],
    'token': config['testproject_token']
  }

  # Initialize the TestProject WebDriver instance
  if config['browser'] == 'Firefox':
    b = webdriver.Firefox(**kwargs)
  elif config['browser'] == 'Chrome':
    b = webdriver.Chrome(**kwargs)
  elif config['browser'] == 'Headless Chrome':
    opts = ChromeOptions()
    opts.add_argument('headless')
    b = webdriver.Chrome(chrome_options=opts, **kwargs)
  else:
    raise Exception(f'Browser "{config["browser"]}" is not supported')

  # Make its calls wait for elements to appear
  b.implicitly_wait(config['implicit_wait'])

  # Return the WebDriver instance for the setup
  yield b

  # Quit the WebDriver instance for the cleanup
  b.quit()

This was the only section of code I needed to change to make my PyCon 2020 tutorial project work with TestProject. I had to change the WebDriver invocations to use the TestProject classes. I also had to add arguments for the project name and developer token, which come from the config file. (Note: you may alternatively set the developer token as an environment variable.)

5. Create page objects

Automated tests could make direct calls to the WebDriver interface to interact with the browser, but WebDriver calls are typically low-level and wordy. The Page Object Model is a much better design pattern. Page object classes encapsulate WebDriver gorp so that tests can call simpler, more readable methods.

The DuckDuckGo search test interacts with two pages: the search page and the result page. The pages package contains a module for each page. Here’s pages/search.py:

from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys


class DuckDuckGoSearchPage:

  URL = 'https://www.duckduckgo.com'

  SEARCH_INPUT = (By.ID, 'search_form_input_homepage')

  def __init__(self, browser):
    self.browser = browser

  def load(self):
    self.browser.get(self.URL)

  def search(self, phrase):
    search_input = self.browser.find_element(*self.SEARCH_INPUT)
    search_input.send_keys(phrase + Keys.RETURN)

And here’s pages/result.py:

from selenium.webdriver.common.by import By

class DuckDuckGoResultPage:
  
  RESULT_LINKS = (By.CSS_SELECTOR, 'a.result__a')
  SEARCH_INPUT = (By.ID, 'search_form_input')

  def __init__(self, browser):
    self.browser = browser

  def result_link_titles(self):
    links = self.browser.find_elements(*self.RESULT_LINKS)
    titles = [link.text for link in links]
    return titles
  
  def search_input_value(self):
    search_input = self.browser.find_element(*self.SEARCH_INPUT)
    value = search_input.get_attribute('value')
    return value

  def title(self):
    return self.browser.title

Notice that this code uses the “regular” WebDriver interface because TestProject’s WebDriver classes extend the Selenium WebDriver classes.

To make setup easier, I added fixtures to tests/conftest.py to construct each page object, too. They call the browser fixture and inject the WebDriver instance into each page object:

from pages.result import DuckDuckGoResultPage
from pages.search import DuckDuckGoSearchPage


@pytest.fixture
def search_page(browser):
  return DuckDuckGoSearchPage(browser)


@pytest.fixture
def result_page(browser):
  return DuckDuckGoResultPage(browser)

6. Automate the test case

All the automation plumbing is finally in place. Here’s the test case in tests/traditional/test_duckduckgo.py:

import pytest


@pytest.mark.parametrize('phrase', ['panda', 'python', 'polar bear'])
def test_basic_duckduckgo_search(search_page, result_page, phrase):
  
  # Given the DuckDuckGo home page is displayed
  search_page.load()

  # When the user searches for the phrase
  search_page.search(phrase)

  # Then the search result query is the phrase
  assert phrase == result_page.search_input_value()
  
  # And the search result links pertain to the phrase
  titles = result_page.result_link_titles()
  matches = [t for t in titles if phrase.lower() in t.lower()]
  assert len(matches) > 0

  # And the search result title contains the phrase
  assert phrase in result_page.title()

I parametrized the test to run it for three different phrases. The test function does not interact with the WebDriver instance directly. Instead, it interacts exclusively with the page objects.

7. Run the tests

The tests run like any other pytest tests: python -m pytest at the command line. If everything is set up correctly, then the tests will run successfully and upload results to the TestProject app.

In the TestProject dashboard, the Reports tab shows all the test you have run. It also shows the different test projects you have.

Check out those results!

You can also drill into results for individual test case runs. TestProject automatically records the browser type, timestamps, pass-or-fail results, and every WebDriver call. You can also download PDF reports!

Results for an individual test

What if … BDD?

I was delighted to see how easily I could run a traditional pytest suite using TestProject. Then, I thought to myself, “What if I could use a BDD test framework?” I personally love Behavior-Driven Development, and Python has multiple BDD test frameworks. There is no reason why a BDD test framework wouldn’t work with TestProject!

So, I rewrote the DuckDuckGo search test as a feature file with step definitions using pytest-bdd. The BDD-style test uses the same fixtures and page objects as the traditional test.

Here’s the Gherkin scenario in tests/bdd/features/duckduckgo.feature:

Feature: DuckDuckGo
  As a Web surfer,
  I want to search for websites using plain-language phrases,
  So that I can learn more about the world around me.


  Scenario Outline: Basic DuckDuckGo Web Search
    Given the DuckDuckGo home page is displayed
    When the user searches for "<phrase>"
    Then the search result query is "<phrase>"
    And the search result links pertain to "<phrase>"
    And the search result title contains "<phrase>"

    Examples:
      | phrase     |
      | panda      |
      | python     |
      | polar bear |

And here’s the step definition module in tests/bdd/step_defs/test_duckduckgo_bdd.py:

from pytest_bdd import scenarios, given, when, then, parsers
from selenium.webdriver.common.keys import Keys


scenarios('../features/duckduckgo.feature')


@given('the DuckDuckGo home page is displayed')
def load_duckduckgo(search_page):
  search_page.load()


@when(parsers.parse('the user searches for "{phrase}"'))
@when('the user searches for "<phrase>"')
def search_phrase(search_page, phrase):
  search_page.search(phrase)


@then(parsers.parse('the search result query is "{phrase}"'))
@then('the search result query is "<phrase>"')
def check_search_result_query(result_page, phrase):
  assert phrase == result_page.search_input_value()


@then(parsers.parse('the search result links pertain to "{phrase}"'))
@then('the search result links pertain to "<phrase>"')
def check_search_result_links(result_page, phrase):
  titles = result_page.result_link_titles()
  matches = [t for t in titles if phrase.lower() in t.lower()]
  assert len(matches) > 0


@then(parsers.parse('the search result title contains "{phrase}"'))
@then('the search result title contains "<phrase>"')
def check_search_result_title(result_page, phrase):
  assert phrase in result_page.title()

There’s one more nifty trick I added with pytest-bdd. I added a hook to report each Gherkin step to TestProject with a screenshot! That way, testers can trace each test case step more easily in the TestProject reports. Capturing screenshots also greatly assists test triage when failures arise. This hook is located in tests/conftest.py:

def pytest_bdd_after_step(request, feature, scenario, step, step_func):
  browser = request.getfixturevalue('browser')
  browser.report().step(description=str(step), message=str(step), passed=True, screenshot=True)

Since pytest-bdd is just a pytest plugin, its tests run using the same python -m pytest command. TestProject will group these test results into the same project as before, but it will separate the traditional tests from the BDD tests by name. Here’s what the Gherkin steps with screenshots look like:

Custom Gherkin step with screenshot reported in the TestProject app

This is Awesome!

As its name denotes, TestProject is a great platform for handling project-level concerns for testing work: reporting, integrations, and fast feedback. Adding TestProject to an existing automation solution feels seamless, and its sleek user experience gives me what I need as a tester without getting in my way. The one word that keeps coming to mind is “simple” – TestProject simplifies setup and sharing. Its design takes to heart the renowned Python adage, “Simple is better than complex.” As such, TestProject’s new Python SDK is a welcome addition to the Python testing ecosystem.

I look forward to exploring Python support for mobile testing with Appium soon. I also look forward to seeing all the new Python add-ons the community will develop.

Beyond Unit Tests: End-to-End Web UI Testing

On October 4, 2019, I gave a talk entitled Beyond Unit Tests: End-to-End Web UI Testing at PyGotham 2019. Check it out below! I show how to write a concise-yet-complete test solution for Web UI test cases using Python, pytest, and Selenium WebDriver.

This talk is a condensed version of my Hands-On Web UI Testing tutorials that I delivered at DjangoCon 2019 and PyOhio 2019. If you’d like to take the full tutorial, check out https://github.com/AndyLPK247/djangocon-2019-web-ui-testing. Full instructions are in the README.

Be sure to check out the other PyGotham 2019 talks, too. My favorite was Dungeons & Dragons & Python: Epic Adventures with Prompt-Toolkit and Friends by Mike Pirnat.

Hands-On UI Testing with Python (SmartBear Webinar)

On August 14, 2019, I teamed up with SmartBear to deliver a one-hour webinar about Web UI testing with Python! It was an honor to work with Nicholas Brown, Digital Marketing Manager for CrossBrowserTesting at SmartBear Software, to make this webinar happen.

The Webinar

Source: https://crossbrowsertesting.com/resources/webinars/testing-with-python

In the webinar, I showed how to build a basic Web UI test automation solution using Python, pytest, and Selenium WebDriver. The tutorial covered automating one test, a simple DuckDuckGo search, from inception to automation. It also showed how to use CrossBrowserTesting to scale the solution so that it can run tests on any browser, any platform, and any version in the cloud as a service!

The example test project for the webinar is hosted in Github here: https://github.com/AndyLPK247/smartbear-hands-on-ui-testing-python

I encourage you to clone the Github repository and try to run the example test on your own! Make sure to get a CrossBrowserTesting trial license so you can try different browsers. You can also try to write new tests of your own. All instructions are in the README. Have fun with it!

The Q&A

After the tutorial, we took questions from the audience. Here are answers to the top questions:

How can we automate UI interactions for CAPTCHA?

CAPTCHA is a feature many websites use to determine whether or not a user is human. Most CAPTCHAs require the user to read obscured text from an image, but there are other variations. By their very nature, CAPTCHAs are designed to thwart UI automation.

When someone asked this question during the webinar, I didn’t have an answer, so I did some research afterwards. Unfortunately, it looks like there’s no easy solution. The best workarounds involve driving apps through their APIs to avoid CAPTCHAs. I also saw some services that offer to solve CAPTCHAs.

Are there any standard Page Object Pattern implementations in Python?

Not really. Mozilla maintains the PyPOM project, but I personally haven’t used it. I like to keep my page objects pretty simple, as shown in the tutorial. I also recommend the Screenplay Pattern, which handles concerns better as test automation solutions grow larger. I’m actually working on a Pythonic implementation of the Screenplay Pattern that I hope to release soon!

How can I run Python tests that use Selenium WebDriver and pytest from Jenkins?

Any major Continuous Integration tool like Jenkins can easily run Web UI tests in any major language. First, make sure the nodes are properly configured to run the tests – they’ll need Python with the appropriate packages. If you plan to use local browsers, make sure the nodes have the browsers and WebDriver executables properly installed. If you plan to use remote browsers (like with CrossBrowserTesting), make sure your CI environment can call out to the remote service. Test jobs can simply call pytest from the command line to launch the tests. I also recommend the “JUnit” pytest option to generate a JUnit-style XML test report because most CI tools require that format for displaying and tracking test results.

How can I combine API and database testing with Web UI testing?

One way to handle API and database testing is to write integration tests separate from Web UI tests. You can still use pytest, but you’d use a library like requests for APIs and SQLAlchemy for databases.

Another approach is to write “hybrid” tests that use APIs and database calls to help Web UI testing. Browsers are notoriously slow compared to direct back-end calls. For example, database calls could pre-populate data so that, upon login, the website already displays stuff to test. Hybrid tests can make tests much faster and much safer.

How can we test mobile apps and browsers using Python?

Even though our tutorial covered desktop-based browser UI interactions, the strategy for testing mobile apps and browsers is the same. Mobile tests need Appium, which is like a special version of WebDriver for mobile features. The Page Object Pattern (or Screenplay Pattern) still applies. CrossBrowserTesting provides mobile platforms, too!

Tutorial: Web Testing Made Easy with Python

Have you ever discovered a bug in a web app? Yuck! Almost everyone has. Bugs look bad, interrupt the user’s experience, and cheapen the web app’s value. Severe bugs can incur serious business costs and tarnish the provider’s reputation.

So, how can we prevent these bugs from reaching users? The best way to catch bugs is to test the web app. However, web UI testing can be difficult: it requires more effort than unit testing, and it has a bad rap for being flaky.

Never fear! Recently, I teamed up with the awesome folks at TestProject to develop a helpful tutorial that makes web UI test automation easy with the power of Python! The tutorial is named Web Testing Made Easy with Python, Pytest and Selenium WebDriver. It is available for free as a set of TestProject blog articles together with a GitHub example project.

In our tutorial, we will build a simple yet robust web UI test solution using Pythonpytest, and Selenium WebDriver. We cover strategies for good test design as well as patterns for good automation code. By the end of the tutorial, you’ll be a web test automation champ! Your Python test project can be the foundation for your own test cases, too.

How can you take the tutorial? Start reading here, and follow the instructions: https://blog.testproject.io/2019/07/16/open-source-test-automation-python-pytest-selenium-webdriver/

I personally want to thank TestProject for this collaboration. TestProject provides helpful tools that can supercharge your test automation. They offer a smart test recorder, a bunch of add-ons that act like test case building blocks, an SDK that can make test automation coding easier, and beautiful analytics to see exactly what the tests are doing. Not only is TestProject a cool platform, but the people with whom I’ve worked there are great. Be sure to check it out!

Python BDD Framework Comparison

Almost every major programming language has BDD test frameworks, and Python is no exception. In fact, Python has several! So, how do they compare, and which one is best? Let’s find out.

Head-to-Head Comparison

behave

behave is one of the most popular Python BDD frameworks. Although it is not officially part of the Cucumber project, it functions very similarly to Cucumber frameworks.

Pros

  • It fully supports the Gherkin language.
  • Environmental functions and fixtures make setup and cleanup easy.
  • It has Django and Flask integrations.
  • It is popular with Python BDD practitioners.
  • Online docs and tutorials are great.
  • It has PyCharm Professional Edition support.

Cons

  • There’s no support for parallel execution.
  • It’s a standalone framework.
  • Sharing steps between feature files can be a bit of a hassle.

pytest-bdd

pytest-bdd is a plugin for pytest that lets users write tests as Gherkin feature files rather than test functions. Because it integrates with pytest, it can work with any other pytest plugins, such as pytest-html for pretty reports and pytest-xdist for parallel testing. It also uses pytest fixtures for dependency injection.

Pros

  • It is fully compatible with pytest and major pytest plugins.
  • It benefits from pytest‘s community, growth, and goodness.
  • Fixtures are a great way to manage context between steps.
  • Tests can be filtered and executed together with other pytest tests.
  • Step definitions and hooks are easily shared using conftest.py.
  • Tabular data can be handled better for data-driven testing.
  • Online docs and tutorials are great.
  • It has PyCharm Professional Edition support.

Cons

  • Step definition modules must have explicit declarations for feature files (via “@scenario” or the “scenarios” function).
  • Scenario outline steps must be parsed differently.

radish

radish is a BDD framework with a twist: it adds new syntax to the Gherkin language. Language features like scenario loops, scenario preconditions, and constants make radish‘s Gherkin variant more programmatic for test cases.

Resources

Logo

Pros

  • Gherkin language extensions empower testers to write better tests.
  • The website, docs, and logo are on point.
  • Feature files and step definitions come out very clean.

Cons

  • It’s a standalone framework with limited extensions.
  • BDD purists may not like the additions to the Gherkin syntax.

lettuce

lettuce is another vegetable-themed Python BDD framework that’s been around for years. However, the website and the code haven’t been updated for a while.

Resources

Logo

../_images/flow.png

Pros

  • Its code is simpler.
  • It’s tried and true.

Cons

  • It lacks the feature richness of the other frameworks.
  • It doesn’t appear to have much active, ongoing support.

freshen

freshen was one of the first BDD test frameworks for Python. It was a plugin for nose. However, both freshen and nose are no longer maintained, and their doc pages explicitly tell readers to use other frameworks.

My Recommendations

None of these frameworks are perfect, but some have clear advantages. Overall, my top recommendation is pytest-bdd because it benefits from the strengths of pytest. I believe pytest is one of the best test frameworks in any language because of its conciseness, fixtures, assertions, and plugins. The 2018 Python Developers Survey showed that pytest is, by far, the most popular Python test framework, too. Even though pytest-bdd doesn’t feel as polished as behave, I think some TLC from the open source community could fix that.

Here are other recommendations:

  • Use behave if you want a robust, clean experience with the largest community.
  • Use pytest-bdd if you need to integrate with other plugins, already have a bunch of pytest tests, or want to run tests in parallel.
  • Use radish if you want more programmatic control of testing at the Gherkin layer.
  • Don’t use lettuce or freshen.

Python Testing 101: pytest-bdd

Warning: If you are new to BDD, then I strongly recommend reading the BDD 101 series before trying to use pytest-bdd. Also, make sure that you are already familiar with the pytest framework.

Overview

pytest-bdd is a behavior-driven (BDD) test framework that is very similar to behaveCucumber and SpecFlow. BDD frameworks are very different from more traditional frameworks like unittest and pytest. Test scenarios are written in Gherkin “.feature” files using plain language. Each Given, When, and Then step is “glued” to a step definition – a Python function decorated by a matching string in a step definition module. This means that there is a separation of concerns between test cases and test code. Gherkin steps may also be reused by multiple scenarios.

pytest-bdd is very similar to other Python BDD frameworks like behave, radish, and lettuce. However, unlike the others, pytest-bdd is not a standalone framework: it is a plugin for pytest. Thus, all of pytest‘s features and plugins can be used with pytest-bdd. This is a huge advantage!

Installation

Use pip to install both pytest and pytest-bdd.

pip install pytest
pip install pytest-bdd

Project Structure

Project structure for pytest-bdd is actually pretty flexible (since it is based on pytest), but the following conventions are recommended:

  • All test code should appear under a test directory named “tests”.
  • Feature files should be placed in a test subdirectory named “features”.
  • Step definition modules should be placed in a test subdirectory named “step_defs”.
  • conftest.py files should be located together with step definition modules.

Other names and hierarchies may be used. For example, large test suites can have feature-specific directories of features and step defs. pytest should be able to discover tests anywhere under the test directory.

[project root directory]
|‐‐ [product code packages]
|-- [test directories]
|   |-- features
|   |   `-- *.feature
|   `-- step_defs
|       |-- __init__.py
|       |-- conftest.py
|       `-- test_*.py
`-- [pytest.ini|tox.ini|setup.cfg]

Note: Step definition module names do not need to be the same as feature file names. Any step definition can be used by any feature file within the same project.

Example Code

An example project named behavior-driven-python located in GitHub shows how to write tests using pytest-bdd. This section will explain how the Web tests are designed.

The top layer for pytest-bdd tests is the set of Gherkin feature files. Notice how the scenario below is concise, focused, meaningful, and declarative:

@web @duckduckgo
Feature: DuckDuckGo Web Browsing
  As a web surfer,
  I want to find information online,
  So I can learn new things and get tasks done.

  # The "@" annotations are tags
  # One feature can have multiple scenarios
  # The lines immediately after the feature title are just comments

  Scenario: Basic DuckDuckGo Search
    Given the DuckDuckGo home page is displayed
    When the user searches for "panda"
    Then results are shown for "panda"

Each scenario step is “glued” to a decorated Python function called a step definition. Step definitions are written in Python test modules, as shown below:

import pytest

from pytest_bdd import scenarios, given, when, then, parsers
from selenium import webdriver
from selenium.webdriver.common.keys import Keys

# Constants

DUCKDUCKGO_HOME = 'https://duckduckgo.com/'

# Scenarios

scenarios('../features/web.feature')

# Fixtures

@pytest.fixture
def browser():
    b = webdriver.Firefox()
    b.implicitly_wait(10)
    yield b
    b.quit()

# Given Steps

@given('the DuckDuckGo home page is displayed')
def ddg_home(browser):
    browser.get(DUCKDUCKGO_HOME)

# When Steps

@when(parsers.parse('the user searches for "{phrase}"'))
def search_phrase(browser, phrase):
    search_input = browser.find_element_by_id('search_form_input_homepage')
    search_input.send_keys(phrase + Keys.RETURN)

# Then Steps

@then(parsers.parse('results are shown for "{phrase}"'))
def search_results(browser, phrase):
    # Check search result list
    # (A more comprehensive test would check results for matching phrases)
    # (Check the list before the search phrase for correct implicit waiting)
    links_div = browser.find_element_by_id('links')
    assert len(links_div.find_elements_by_xpath('//div')) > 0
    # Check search phrase
    search_input = browser.find_element_by_id('search_form_input')
    assert search_input.get_attribute('value') == phrase

Notice how each Given/When/Then step has a function with an appropriate decorator. Arguments, such as the search “phrase,” may also be passed from step to function. pytest-bdd provides a few argument parsers out of the box and also lets programmers implement their own. (By default, strings are compared using equality.) One function can be decorated for many steps, too.

pytest fixtures may also be used by step functions. The code above uses a fixture to initialize the Firefox WebDriver before each scenario and then quit it after each scenario. Fixtures follow all the same rules, including scope. Any step function can use a fixture by declaring it as an argument. Furthermore, any “@given” step function that returns a value can also be used as a fixture. Please read the official docs for more info about fixtures with pytest-bdd.

One important, easily-overlooked detail is that scenarios must be explicitly declared in test modules. Unlike other BDD frameworks that treat feature files as the main scripts, pytest-bdd treats the “test_*.py” module as the main scripts (because that’s what pytest does). Scenarios may be specified explicitly using scenario decorators, or all scenarios in a list of feature files may be included implicitly using the “scenarios” shortcut function shown above.

To share steps across multiple feature files, add them to the “conftest.py” file instead of the test modules. Since scenarios must be declared within a test module, they can only use step functions available within the same module or in “conftest.py”. As a best practice, put commonly shared steps in “conftest.py” and feature-specific steps in the test module. The same recommendation also applies for hooks.

Scenario outlines require special implementation on the Python side to run successfully. Unfortunately, steps used by scenario outlines need unique step decorators and extra converting. Please read the official docs or the example project to see examples.

Test Launch

pytest-bdd can leverage the full power of pytest. Tests can be run in full or filtered by tag. Below are example commands using the example project:

# run all tests
pytest

# filter tests by test module
# note: feature files cannot be run directly
pytest tests/step_defs/test_unit_basic.py
pytest tests/step_defs/test_unit_outlines.py
pytest tests/step_defs/test_unit_service.py
pytest tests/step_defs/test_unit_web.py

# filter tests by tags
# running by tag is typically better than running by path
pytest -k "unit"
pytest -k "service"
pytest -k "web"
pytest -k "add or remove"
pytest -k "unit and not outline"

# print JUnit report
pytest -junitxml=/path/for/output

pytest-bdd tests can be executed and filtered together with regular pytest tests. Tests can all be located within the same directory. Tags work just like pytest.mark. As a warning, marks must be explicitly added to “pytest.ini” starting with pytest 5.0.

All other pytest plugins should work, too. For example:

Pros and Cons

Just like for other BDD frameworks, pytest-bdd is best suited for black-box testing because it forces the developer to write test cases in plain, descriptive language. In my opinion, it is arguably the best BDD framework currently available for Python because it rests on the strength and extendability of pytest. It also has PyCharm support (in the Professional Edition). However, it can be more cumbersome to use than behave due to the extra code needed for declaring scenarios, implementing scenario outlines, and sharing steps. Nevertheless, I would still recommend pytest-bdd over behave for most users because it is more powerful – pytest is just awesome!

Book Review: pytest Quick Start Guide

tl;dr

Title pytest Quick Start Guide
(available from Packt and Amazon)
Author Bruno Oliveira (@nicoddemus)
Publication 2018 Packt Publishing
Summary “Write better Python code with simple and maintainable tests” – a very readable guide for pytest’s main features.
Prerequisites Intermediate-level Python programming.

Summary

pytest Quick Start Guide is a new book about using pytest for Python test automation. Bruno Oliveira explains not only how to use pytest but also why its features are useful. Even though this book is written as a “quick start” introduction, it nevertheless dives deep into pytest’s major features. It covers:

  1. An introduction to pytest
  2. Why pytest is superior to unittest
  3. Setting up pytest with pip and virtualenv
  4. Writing basic tests
  5. How assertions really work
  6. Common command line arguments
  7. Marks and parametrization
  8. Using and writing fixtures
  9. Popular plugins
  10. Converting unittest suites to pytest

Example code is hosted on GitHub at PacktPublishing/pytest-Quick-Start-Guide.

Praises

This book is an easy introduction to test automation in Python with pytest. Readers should have intermediate-level Python skills, but they do not need previous testing or automation skills. The progression of chapters makes it easy to start quick and then go deeper. Oliveira has a very accessible writing style, too.

The unittest refactoring guide is a hidden gem. The unittest module is great because it comes bundled with Python, but it is also clunky and not very Pythonic. Not all teams know the best way to modernize their test suites. Oliveira provides many pieces of practical advice for making the change, at varying degrees of conversion. The big changes use pytest’s fixtures and assertions.

A Comparison to Python Testing with pytest

Python Testing with pytest by Brian Okken is another popular pytest book. Both books are great resources for learning pytest, but each approaches the framework from a unique perspective. How do they compare? Here’s what I saw:

Oliveira’s book Okken’s book
Oliveira’s book is a great introductory guide for pytest. Oliveira’s writing style makes the reader feel like the author is almost teaching in person. The book’s main theme is getting the reader to use the main features of pytest pragmatically for general testing needs. The main differences in content are the unittest refactoring guide and some of the plugins and fixtures covered. This book is probably the best choice for beginners who want to learn the basics of pytest quickly. Okken’s book is introductory but also a great manual for future reference. Okken’s writing style is direct and concise, which covers more material in fewer pages. The format for each chapter is consistent: for each idea: idea → code → output → explanation; exercises at the end. Okken also covers how to create and share custom pytest plugins. This book is probably the best choice for people who want to master the ins and outs of pytest.

Ultimately, I recommend both books because they are both excellent.

Takeaways

Reading books on the same subject by different authors helps the reader learn the subject better. I’ve used pytest quite a lot myself, but I was able to learn new things from both pytest Quick Start Guide and Python Testing with pytest. Reading how experts use and think about the framework makes me a better engineer. Different writing styles and different opinions also challenge my own understandings. (It’s also funny that the authors of both pytest books have the same initials – “B.O.”)

pytest is really popular. There are now multiple good books on the subject. It’s becoming the de facto test automation framework for Python, outpacing unittest, nose, and others. These days, it seems more popular to write a pytest plugin than to create a new framework.

Book Review: Python Testing with pytest

tl;dr

Title Python Testing with pytest
Author Brian Okken (@brianokken)
Publication 2017 (The Pragmatic Programmers)
Summary How to use all the features of pytest for Python test automation – “simple, rapid, effective, and scalable.”
Prerequisites Intermediate-level Python programming.

Summary

Python Testing with pytest is the book on pytest*. Brian Okken covers all the ins and outs of the framework. The book is useful both as tutorial for learning pytest as well as a reference for specific framework features. It covers:

  • Getting started with pytest
  • Writing simple tests as functions
  • Writing more interesting tests with assertions, exceptions, and parameters
  • Using all the different execution options
  • Writing fixtures to flexibly separate concerns and reuse code
  • Using built-in fixtures like tmpdir, pytestconfig, and monkeypatch
  • Using configuration files to control execution
  • Integrating pytest with other tools like pdb, tox, and Jenkins

Appendices also cover:

  • Using Python virtual environments
  • Installing packages with pip
  • An overview of popular plugins like pytest-xdist and pytest-cov
  • Packaging and distributing Python packages

[* Update on 9/27/2018: Check out my review on another excellent pytest book, pytest Quick Start Guide by Bruno Oliveira.]

Praises

This book is a comprehensive guide to pytest. It thoroughly covers the framework’s features and gives pointers to more info elsewhere. Even though pytest has excellent online documentation, I still recommend this book to anyone who wants to become a pytest master. Online docs tend to be fragmented with each piece limited in scope, whereas books like this one are designed to be read progressively and orderly for maximal understanding of the material.

I love how this book is example-driven. Each section follows a simple yet powerful outline: idea → code → output → explanation. Having real code with real output truly cements the point of each mini-lesson. New topics are carefully unfolded so that they build upon previous topics, making the book read like a collection of tutorials. Examples at the end of every chapter challenge the readers to practice what they learn. The formatting of each section also looks great.

The extra info on related topics like pip and virtualenv is also a nice touch. Python pros probably don’t need it, but beginners might get stuck without it.

The rocket ship logo on the cover is also really cool!

Takeaways

pytest is one of the best functional test frameworks currently available in any language. It breaks the clunky xUnit mold, in which class structures were awkwardly superimposed over test cases as if one size fits all. Instead, pytest is lightweight and minimalist because it relies on functions and fixtures. Scope is much easier to manage, code is more reusable, and side effects can more easily be avoided. pytest has taken over Python testing because it is so Pythonic.

Brian’s concise writing style has also inspired me to be more direct in my own writing. I tend to be rather verbose out of my desire to be descriptive. However, fewer words often leave a more powerful impression. They also make the message easier to comprehend. Python is beloved for its concise expressiveness, and as a Pythonista, it would be fitting for me to adopt that trait into my English.

If I had a wish list for a second edition, I’d love to see more info about assertions and other plugins (namely pytest-bdd). I think an appendix with comparisons to other Python test frameworks would also be valuable.

A Warning

I ordered a physical copy of this book directly from Amazon (not a third-party seller). Unfortunately, that copy was missing all the introductory content: the table of contents, the acknowledgements, and the preface. The first page after the front cover was Chapter 1. Befuddled, I reached out to Brian Okken (who I personally met at PyCon 2018). We suspected that it was either a misprint or a bootleg copy. Either way, we sent the evidence to the publisher, and Amazon graciously exchanged my defective copy for the real deal. Please look out for this sort of problem if you purchase a printed copy of this book for yourself!

If you want to learn more about pytest, please read my article Python Testing 101: pytest.

EGAD! How Do We Start Writing (Better) Tests?

Some have never automated tests and can’t check themselves before they wreck themselves. Others have 1000s of tests that are flaky, duplicative, and slow. Wa-do-we-do? Well, I gave a talk about this problem at a few Python conferences. The language used for example code was Python, but the principles apply to any language.

Here’s the PyTexas 2019 talk:

And here’s the PyGotham 2018 talk:

And here’s the first time I gave this talk, at PyOhio 2018:

I also gave this talk at PyCaribbean 2019 and PyTennessee 2020 (as an impromptu talk), but it was not recorded.

Python Testing 101: pytest

Overview

pytest is an awesome Python test framework. According to its homepage:

pytest is a mature full-featured Python testing tool that helps you write better programs.

Pytests may be written either as functions or as methods in classes – unlike unittest, which forces tests to be inside classes. Test classes must be named “Test*”, and test functions/methods must be named “test_*”. Test classes also need not inherit from unittest.TestCase or any other base class. Thus, pytests tend to be more concise and more Pythonic. pytest can also run unittest and nose tests.

pytest provides many advanced test framework features:

pytest is actively supported for both Python 2 and 3.

Installation

Use pip to install the pytest module. Optionally, install other plugins as well.

pip install pytest
pip install pytest-cov
pip install pytest-xdist
pip install pytest-bdd

Project Structure

The modules containing pytests should be named “test_*.py” or “*_test.py”. While the pytest discovery mechanism can find tests anywhere, pytests must be placed into separate directories from the product code packages. These directories may either be under the project root or under the Python package. However, the pytest directories must not be Python packages themselves, meaning that they should not have “__init__.py” files. (My recommendation is to put all pytests under “[project root]/tests”.) Test configuration may be added to configuration files, which may go by the names “pytest.ini”, “tox.ini”, or “setup.cfg”.

[project root directory]
|‐‐ [product code packages]
|-- [test directories]
|   |-- test_*.py
|   `-- *_test.py
`-- [pytest.ini|tox.ini|setup.cfg]

Example Code

An example project named example-py-pytest is located in my GitHub python-testing-101 repository. The project has the following structure:

example-py-pytest
|-- com.automationpanda.example
|   |-- __init__.py
|   |-- calc_class.py
|   `-- calc_func.py
|-- tests
|   |-- test_calc_class.py
|   `-- test_calc_func.py
|-- README.md
`-- pytest.ini

The pytest.ini file is simply a configuration file stub. Feel free to add contents for local testing needs.

# Add pytest options here
[pytest]

view raw
pytest.ini
hosted with ❤ by GitHub

The com.automationpanda.example.calc_func module contains basic math functions.

def add(a, b):
return a + b
def subtract(a, b):
return a b
def multiply(a, b):
return a * b
def divide(a, b):
return a * 1.0 / b
def maximum(a, b):
return a if a >= b else b
def minimum(a, b):
return a if a <= b else b

view raw
calc_func.py
hosted with ❤ by GitHub

The calc_func tests located in tests/test_calc_func.py are written as functions. Test functions are preferable to test classes when testing functions without side effects.

import pytest
from com.automationpanda.example.calc_func import *
NUMBER_1 = 3.0
NUMBER_2 = 2.0
def test_add():
value = add(NUMBER_1, NUMBER_2)
assert value == 5.0
def test_subtract():
value = subtract(NUMBER_1, NUMBER_2)
assert value == 1.0
def test_subtract_negative():
value = subtract(NUMBER_2, NUMBER_1)
assert value == 1.0
def test_multiply():
value = multiply(NUMBER_1, NUMBER_2)
assert value == 6.0
def test_divide():
value = divide(NUMBER_1, NUMBER_2)
assert value == 1.5

view raw
test_calc_func.py
hosted with ❤ by GitHub

The divide-by-zero test uses pytest.raises:

def test_divide_by_zero():
with pytest.raises(ZeroDivisionError) as e:
divide(NUMBER_1, 0)
assert "division by zero" in str(e.value)

view raw
test_calc_func.py
hosted with ❤ by GitHub

And the min/max tests use parameterization:

@pytest.mark.parametrize("a,b,expected", [
(NUMBER_1, NUMBER_2, NUMBER_1),
(NUMBER_2, NUMBER_1, NUMBER_1),
(NUMBER_1, NUMBER_1, NUMBER_1),
])
def test_maximum(a, b, expected):
assert maximum(a, b) == expected
@pytest.mark.parametrize("a,b,expected", [
(NUMBER_1, NUMBER_2, NUMBER_2),
(NUMBER_2, NUMBER_1, NUMBER_2),
(NUMBER_2, NUMBER_2, NUMBER_2),
])
def test_minimum(a, b, expected):
assert minimum(a, b) == expected

view raw
test_calc_func.py
hosted with ❤ by GitHub

The com.automationpanda.example.calc_class module contains the Calculator class, which uses the math functions from calc_func. Keeping the functional spirit, the private _do_math method takes in a reference to the math function for greater code reusability.

from com.automationpanda.example.calc_func import *
class Calculator(object):
def __init__(self):
self._last_answer = 0.0
@property
def last_answer(self):
return self._last_answer
def _do_math(self, a, b, func):
self._last_answer = func(a, b)
return self.last_answer
def add(self, a, b):
return self._do_math(a, b, add)
def subtract(self, a, b):
return self._do_math(a, b, subtract)
def multiply(self, a, b):
return self._do_math(a, b, multiply)
def divide(self, a, b):
return self._do_math(a, b, divide)
def maximum(self, a, b):
return self._do_math(a, b, maximum)
def minimum(self, a, b):
return self._do_math(a, b, minimum)

view raw
calc_class.py
hosted with ❤ by GitHub

While tests for the Calculator class could be written using a test class, pytest test functions are just as capable. Fixtures enable a more fine-tuned setup/cleanup mechanism than the typical xUnit-like methods found in test classes. Fixtures can also be used in conjunction with parameterized methods. The tests/test_calc_class.py module is very similar to tests/test_calc_func.py and shows how to use fixtures for testing a class.

import pytest
from com.automationpanda.example.calc_class import Calculator
# "Constants"
NUMBER_1 = 3.0
NUMBER_2 = 2.0
# Fixtures
@pytest.fixture
def calculator():
return Calculator()
# Helpers
def verify_answer(expected, answer, last_answer):
assert expected == answer
assert expected == last_answer
# Test Cases
def test_last_answer_init(calculator):
assert calculator.last_answer == 0.0
def test_add(calculator):
answer = calculator.add(NUMBER_1, NUMBER_2)
verify_answer(5.0, answer, calculator.last_answer)
def test_subtract(calculator):
answer = calculator.subtract(NUMBER_1, NUMBER_2)
verify_answer(1.0, answer, calculator.last_answer)
def test_subtract_negative(calculator):
answer = calculator.subtract(NUMBER_2, NUMBER_1)
verify_answer(1.0, answer, calculator.last_answer)
def test_multiply(calculator):
answer = calculator.multiply(NUMBER_1, NUMBER_2)
verify_answer(6.0, answer, calculator.last_answer)
def test_divide(calculator):
answer = calculator.divide(NUMBER_1, NUMBER_2)
verify_answer(1.5, answer, calculator.last_answer)
def test_divide_by_zero(calculator):
with pytest.raises(ZeroDivisionError) as e:
calculator.divide(NUMBER_1, 0)
assert "division by zero" in str(e.value)
@pytest.mark.parametrize("a,b,expected", [
(NUMBER_1, NUMBER_2, NUMBER_1),
(NUMBER_2, NUMBER_1, NUMBER_1),
(NUMBER_1, NUMBER_1, NUMBER_1),
])
def test_maximum(calculator, a, b, expected):
answer = calculator.maximum(a, b)
verify_answer(expected, answer, calculator.last_answer)
@pytest.mark.parametrize("a,b,expected", [
(NUMBER_1, NUMBER_2, NUMBER_2),
(NUMBER_2, NUMBER_1, NUMBER_2),
(NUMBER_2, NUMBER_2, NUMBER_2),
])
def test_minimum(calculator, a, b, expected):
answer = calculator.minimum(a, b)
verify_answer(expected, answer, calculator.last_answer)

view raw
test_calc_class.py
hosted with ❤ by GitHub

Personally, I prefer to write pytests as functions because they are usually cleaner and more flexible than classes. Plus, test functions appeal to my affinity for functional programming.

Test Launch

Basic Test Execution

pytest has a very powerful command line for launching tests. Simply run the pytest module from within the project root directory, and pytest will automatically discover tests.

# Find and run all pytests from the current directory
python -m pytest

# Run pytests under a given path
python -m pytest 

# Run pytests in a specific module
python -m pytest tests/test_calc_func.py

# Generate JUnit-style XML test reports
python -m pytest --junitxml=[path-to-file]

# Get command help
python -m pytest -h

The terminal output looks like this:

python -m pytest
=============================== test session starts ===============================
platform darwin -- Python 2.7.13, pytest-3.0.6, py-1.4.32, pluggy-0.4.0
rootdir: /Users/andylpk247/Programming/automation-panda/python-testing-101/example-py-pytest, inifile: pytest.ini
plugins: cov-2.4.0
collected 25 items

tests/test_calc_class.py .............
tests/test_calc_func.py ............

============================ 25 passed in 0.11 seconds ============================

pytest also provides shorter “pytest” and “py.test” command that may be run instead of the longer “python -m pytest” module form. However, the shorter commands do not append the current path to PYTHONPATH, meaning modules under test may not be importable. Make sure to update PYTHONPATH before using the shorter commands.

# Update the Python path
PYTHONPATH=$PYTHONPATH:.

# Discover and run tests using the shorter command
pytest

Code Coverage

To run code coverage with the pytest-cov plugin module, use the following command. The report types are optional, but all four types are show below. Specific paths for each report may be appended using “:”.

# Run tests with code coverage
python -m pytest [test-path] [other-options] \
      --cov= \
      --cov-report=annotate \
      --cov-report=html \
      --cov-report=term \
      --cov-report=xml

Code coverage output on the terminal (“term” cov-report) looks like this:

python -m pytest --cov=com --cov-report=term
============================= test session starts ==============================
platform darwin -- Python 3.6.5, pytest-3.0.6, py-1.4.32, pluggy-0.4.0
rootdir: /Users/andylpk247/Programming/automation-panda/python-testing-101/example-py-pytest, inifile: pytest.ini
plugins: cov-2.4.0
collected 25 items 

tests/test_calc_class.py .............
tests/test_calc_func.py ............

---------- coverage: platform darwin, python 3.6.5-final-0 -----------
Name                                        Stmts   Miss  Cover
---------------------------------------------------------------
com/__init__.py                                 0      0   100%
com/automationpanda/__init__.py                 0      0   100%
com/automationpanda/example/__init__.py         0      0   100%
com/automationpanda/example/calc_class.py      21      0   100%
com/automationpanda/example/calc_func.py       12      0   100%
---------------------------------------------------------------
TOTAL                                          33      0   100%

========================== 25 passed in 0.11 seconds ===========================<span id="mce_SELREST_start" style="overflow:hidden;line-height:0;"></span>

Parallel Testing

Parallel testing is vital for more intense testing, such as web testing. The pytest-xdist plugin makes it possible both to scale-up tests by running more than one test process and to scale-out by running tests on other machines. (As a prerequisite, machines need rsync and SSH.) The command below shows how to run multiple test sub-processes; refer to official documentation for multi-machine setup

python -m pytest -n 4
============================= test session starts ==============================
platform darwin -- Python 3.6.5, pytest-3.0.6, py-1.4.32, pluggy-0.4.0
rootdir: /Users/andylpk247/Programming/automation-panda/python-testing-101/example-py-pytest, inifile: pytest.ini
plugins: xdist-1.22.2, forked-0.2, cov-2.4.0
gw0 [25] / gw1 [25] / gw2 [25] / gw3 [25]
scheduling tests via LoadScheduling
.........................
========================== 25 passed in 1.30 seconds ===========================

Pros and Cons

I’ll say it again: pytest is awesome. It is a powerful test framework with many features, yet its tests are concise and readable. It is very popular and actively supported for both versions of Python. It can handle testing at the unit, integration, and end-to-end levels. It can also be extended with plugins, notably ones for code coverage, parallel execution, and BDD. The only challenge with pytest is that advanced features (namely fixtures) have a learning curve.

My recommendation is to use pytest for standard functional testing in Python. It is one of the best and most popular test frameworks available, and it beats the pants off of alternatives like unittest and nosepytest is my go-to Python framework, period.

This article is meant to be an introduction. Check out Python Testing with pytest by Brian Okken for deeper study.

 

Update: On 4/21/2018, I added pytest-xdist and pytest-bdd plugins, and I made some cosmetic changes.

Update: On 7/29/2018, I added the book recommendation for “Python Testing with pytest.”