Pytest With Eric

Learn to write production level Python Unit Tests with Pytest

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Have you ever had tests fail due to irrelevant or outdated code?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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Almost every backend system uses a database today. After all, you need to persist system and user information.

But with that comes a challenge, your code needs to be able to efficiently read and write from a database. And it’s your job to thoroughly test it.

Databases are capable of smoothly handling read/writes, transactions and rollbacks.

So how do you test your interactions with the database?

How do you leverage the inbuilt database features to write robust I/O operations?

What about handling errors, rollbacks, transactions or network failures? Can you test for these?

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Have you ever spent more time fiddling with test settings than actually writing tests?

Maybe you’ve been through the Pytest docs and seen a bunch of config (short for Configuration) files like pytest.ini, tox.ini, pyproject.toml, and setup.cfg and wondered what they are and how to use them?

Or perhaps you’re familiar with these files but did you know you can also define Pytest config in files like pyproject.toml and setup.cfg?

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Say you’re working on a large test suite with a bunch of tests, each with its own set of requirements and dependencies, across different environments.

You want to run initialization commands in different environments, set up global variables, maybe slack notifications or ping an external API via a webhook to say your tests have begun.

How do you do this in Pytest? How do you tune Pytest to automatically execute tasks before running your tests?

After all you don’t want to write another script to do this. That’s just more code to maintain and more things to go wrong.

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Imagine this, you’ve just joined a company and your first task is to optimise the existing test suite and improve the CI/CD run time.

Or maybe you’re a veteran Python developer and with success, your test suite has grown substantially to 1000 tests, maybe 2000 tests?

Now it’s getting out of control and each run takes 5 mins or more. CI/CD Pipelines cost a small fortune.

You’re testing backend APIs, data ETL processes, user authentication, disk writes and so on.

How do you optimize test run time? Where do you start?

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With time-dependent code, a key challenge arises in testing it across various time and dates.

Consider a scenario where you’re working on a global transaction application. This application involves several time-dependent activities such as recording transaction time, date, the timezone of the transaction, and more.

How do you ensure your application functions correctly when a transaction occurs outside your country?

One approach might be to manually adjust the time, date, and timezone of your system. However, this method is non-deterministic, time-consuming, and impractical for running individual tests.

So, what’s the solution?

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With Pytest, one of the tricker aspects can be understanding and controlling the standard output (stdout) and standard error (stderr).

Often, during testing, your code generates output or errors that are directed to the console.

This can lead to cluttered and obscure test outputs, making it challenging to decipher test results and debug effectively.

So how do you control what’s shown on the console and make your tests easier to debug?

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