You’ve written code and Unit tests, and want to make sure it works. You simply run the pytest command in your terminal to run them the tests. Boom! some tests fail.
How do you debug it?
To debug, it’s sometimes helpful to run one test, run tests in a specific module or class, or run tests based on a marker.
But how do you run just a single test?
In our fast-paced world, every millisecond matters and user experience is paramount.
The importance of faster code faster cannot be overstated.
Beyond correct functioning, it’s imperative to ensure that it operates efficiently and consistently across varying workloads.
This is where performance testing and benchmarking step in to uncover bottlenecks, inefficiencies, and regressions.
Writing repeat code is likely one of the things you least enjoy. At least it’s the case for me.
In a world striving for time, cost and resource efficiency less is more.
How can we apply this same philosophy to Unit Testing?
We often need to reuse resources (like input data, database connections, shared objects and so on) across our Unit Tests.
As a Python developer, you’re likely familiar with Pytest, the popular Unit Testing framework.
It’s a powerful tool to test your Python programs using easy and concise syntax, with a plethora of built-in functionality and Pytest plugins to enhance your testing experience.
Most developers use the CLI to run tests. But it’s actually possible (and easier) to run tests with just a single mouse click. You might be wondering, “Really? But how?”.
If you’re using VS Code then you can set it up in just a few minutes. Saving you countless hours in iterative development and testing time.
If you’re a developer using TDD (test-driven development), you’re familiar with the importance of Unit Tests.
Perhaps you want to test several sets of input data and edge cases. Imagine writing a unit test for each of the infinite inputs.
Fortunately, there’s a better way.
As a Python developer, you may face challenges when executing tests, especially with complex directory structures.
Perhaps you faced a ModuleNotFoundError or ImportErrorwhen running your tests and wondering why Pytest cannot import the modules.
Software testing is critical to your development process, ensuring your code works as expected.
As Python continues to gain popularity as a backend and scripting language, choosing the right testing framework becomes increasingly important.
Two prominent options in the Python ecosystem are Unittest and Pytest. Both frameworks provide powerful capabilities for writing and executing tests but differ in approach, features, and community support.
In this article, we delve into the comparison between Unittest vs Pytest, shedding light on their strengths, weaknesses, and distinctive features.
While developing software, keeping track of events is crucial.
Logging helps you understand the execution flow of your code, to help catch bugs when they happen.
Although Pytest is great testing framework, it doesn’t automatically display the output of print statements or logs, which can be a problem when trying to debug failing tests or understanding flow.
So how do you go about logging and viewing events during Testing?
Can you override the default logging behavior set in the source code, just for testing?
What if you want to output logs to a file instead of the console?
As software engineers, handling errors is an important part of code development.
How often do users behave unexpectedly? More often than not. In most respect, people, systems and the Universe are random.
Maybe your code expects user input or takes data from a received packet and transforms it, perhaps performing complex calculations.
Network delays, even reordering or corrupted data. Whatever be it, it’s good to plan for the worst.
Javascript Object Notation (JSON) is arguably one of the most popular data exchange formats over the web.
Web services use serialisation to convert data from low-level data structures to JSON format that allows receiving services to easily deserialise it.
When writing Unit tests the need for testing JSON input and outputs is one of high importance.
Test data, API Responses and sometimes even config files are defined in JSON which makes it necessary to understand how to read and write to it, using Pytest.