Pytest With Eric

Learn to write production level Python Unit Tests with Pytest

In modern computing, it’s a common belief that computers execute tasks in parallel, effortlessly juggling multiple operations at once.

True parallelism is largely the domain of supercomputers and quantum computing.

But what about the everyday applications that form the backbone of our digital lives?

Asynchronous, or async, allows a program to efficiently execute its tasks (executing other tasks one while is waiting). This is also called concurrency.

When it comes to testing async code, things can get a bit tricky.

How can you ensure your async code behaves as expected? How do you test the interaction of different async tasks and functions without getting tangled in the complexities of event loops and await statements?

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Have you ever had to wade through hundreds or thousands of tests to run a few specific ones?

Running pytest in the terminal, executes ALL the tests which not only takes a long time, but is also inefficient.

Maybe you can run a test in a specific directory but that too carries overhead and is hard to do repeatedly for tests in different directories.

So how do you filter tests in Pytest? How can you tell Pytest to run only the tests you’re interested in? Something like a regular expression?

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Dealing with failing tests is a misery.

Imagine your new code breaks old tests and you have no idea why, preventing you from merging your latest code.

Your tests are slow and break every time you change the code even slightly, feeling more like a massive dumbbell you dread when merging PRs, rather than a net or signal to protect or guide you.

On the other side of the spectrum, maybe you don’t even know what to test.

Should you test every function in your application? What if the code interacts with a database or external service? Should you use mocks?

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Have you heard about Test-Driven Development (TDD) but have no idea what it means?

Maybe you know what it is in theory, but never applied it in practice.

Does the expectation of writing tests before code cause stress or anxiety that you’re wasting time in lost productivity?

What about if you’re working at a start-up and the code will be irrelevant in a few months? — Should you still practice TDD?

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Have you ever wanted to run a single unit test only to find yourself running the entire test suite every time, wasting valuable time and resources?

Running a single test is entirely possible with Pytest, but it’s not as straightforward or user-friendly.

What if you want to define configuration for your tests, such as environment variables, custom arguments, verbosity and run them with a single click repeatedly?

How about if you wanted to add a debugger or line breakpoints to your tests?

You’d kinda struggle to do all of this in the terminal without some advanced command-line flags, what if there was an easier way?

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Have you ever thought about testing your software behavior from a user’s perspective?

You’re probably familiar with unit tests, maybe even integration tests and regression tests. But what about the entire feature and its behavior?

For example, the user logs in, adds products to a shopping cart, and checks out.

How do you validate this entire end-to-end scenario? There could be infinite scenarios.

How to bridge the gap between product engineers, QA testers, and developers — to make sure you’re speaking the same language?

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Have you ever struggled with testing command-line arguments for your applications or scripts?

Perhaps you’ve build a robust application, with a database and REST API and interfaced via command-line (CLI).

You’ve tested the database and REST API, but what about the CLI?

How do you test that your code correctly handles missing arguments, wrong data types, or invalid strings or characters?

Command Line Arguments are a prime error candidate for errors, given their immense interaction with the end user. Hence, its crucial to ensure your application correctly processes user inputs and handles errors gracefully.

How do you do this without redefining all arguments in your tests? How do you abstract that layer?

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As a website test automation engineer, have you ever struggled with repeatedly doing the same UI/UX actions?

Human manual testing is error-prone, time-consuming and in fact… just boring.

It also lacks scale - testing time grows linearly with complexity and number of tests.

Feedback is slow and fairly manual, introducing many unknown variables due to human senses and memory.

So how can you improve this? How can you automate Web Browser UI testing?

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Have you ever wondered how to customize the behaviour of your test suite in Pytest?

Perhaps you want to set a configfile path or read it, before executing your tests?

Maybe set some global variables for Dev or Prod or send Slack notifications after the test suite has finished running?

How do you achieve this in Pytest?

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Have you ever had tests fail due to irrelevant or outdated code?

Perhaps adding to the confusion of debugging and troubleshooting along with unnecessary test execution time.

Or maybe you have code in development that’s not ready to be tested yet.

In such cases, you might want to exclude certain tests from your test suite.

But how do you do it in Pytest? You can of course run pytest in the relevant directories but that’s not very efficient.

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