silkie VS pytest

Last week, I already set up some automation tests for Silkie, my static site generator (SSG). Instead of running tests manually on each Pull Request (PR), I made an attempt to configure GitHub Actions to automate this Continuous Integration (CI) workflow. Moreover, I also helped my friend, Luke, add a test case to his SSG this week.

According to my parter's issue, I create a new test file named. I pull a new PR, the partner's Actions passed it. Before that, I found that many projects have the function of automatic error checking. I wonder how to do it. After lab9, I also created my own GitHub actions. I'm very excited.

After setting up static analysis tools last week, it's time to configure a testing framework for Continuous Integration (CI). There are several options for Silkie, my work-in-progress static site generator, but I decided to give Pytest a try. In this blog, I'll show you how I set up:

If you are tired of maintaining your coding style, I have good news for you. Fortunately, there are developer tools that can automate and streamline mundane development tasks. In this blog, I'll show you how I integrated 2 static code analysis tools and a package manager for pre-commit hooks into Silke, my work-in-progress static site generator.

After wandering the world of static site generators (SSG), I came across an eye-catching, well-documented, and developer-friendly one focusing on documentation sites: Docusaurus. After diving a bit deeper into their documentation, I realized they have many out-of-the-box features, which I can try integrating into Silke, an SSG I wrote from scratch.

This week, I noticed that some functions in my static site generator (SSG) were hardcoded with complex logic and "magic values", so I decided to focus on refactoring them. Without cleaning them up, maintaining them would be a tragedy. For instance, there was a function spanning 36 lines of code with 8 if/elif statements. Some of the statements even have nested if/elif statements themselves. You can find the function referenced in this issue.

This week on my Open Source journey, I attempted to add support for JSON formatted configuration files for an open source Static Site Generator (SSG). The owner of the repo, Tengzhen, also contributed the same feature to my SSG, Silkie. However, I made a step forward by testing his code from a tracking branch before merging it.

As for Eugene, he also contributed the same feature to Silkie, my SSG. I noticed his code might need to be fixed and refactored, so we worked together on both Slack and GitHub to resolve those issues. Given our time constraint and Eugene's lack of experience with Python, it was a success that we managed to add a new feature without breaking the existing ones.

Not long after he started, the first issue was found. As expected, all I could say was:

Pytest: is a third-party testing framework that supports fixtures, parameterized testing, and easy test discovery while having room to add plugins to extend its functionality.

Next, you need to install a testing framework that will be used for performing unit testing in your project. Several testing frameworks are available depending on the programming language used to create an application. For example, JUnit is commonly used for Java apps, pytest for Python apps, NUnit for .NET apps, Jest for JavaScript apps, and so on. We’ll use the Jest framework for this tutorial since we are using JavaScript.

Yea, read through the pytest docs.

Since ITK's inception in 1999, there has been a focus on engineering practices that result in high-quality software. High-quality scientific software is driven by regression testing. The ITK project supported the development of CTest and CDash unit testing and software quality dashboard tools for use with the CMake build system. In the Python programming language, the pytest test driver helps developers write small, readable scripts that ensure their software will continue to work as expected. However, pytest can only test Python scripts by default, and errors in untested computational notebooks are more common than well-tested Python code.

Inorder to have this project be as well-rounded as possible we'll code a series of tests to test the code using pytest. Create a new directory called tests and create a new file called test_db.py in the tests directory. Inside create a test_players.py file.

that doesn't make much sense and there are necessary uses for eval() /exec(), mostly for dynamic creation of code:

For example here's Python dataclasses in the standard library using exec() to create the `__init__` and other methods that go on your dataclass:

https://github.com/python/cpython/blob/main/Lib/dataclasses....

Here's Pydantic using it for a jupyter notebook check:

https://github.com/pydantic/pydantic/blob/594effa279668bd955...

here's Pytest using it to rewrite modules so that functions like assert etc. are instrumented by pytest:

https://github.com/pytest-dev/pytest/blob/eca93db05b6c5ec101...

Here's the decorator module using it (as is the only way to do this in Python) to create a signature matching decorator for an arbitrary function:

https://github.com/micheles/decorator/blob/ad013a2c1ad796996...

All of these libraries are completely secure as eval/exec are used with code fragments that are generated by the libraries, not based on untrusted input.

eval() /exec() are not running executable files, just Python code, the same way all the rest of the package is already doing.

For brevity we will test the numbers from 0 through 10, below are a collection of pytest tests covering these cases. Thus, in test_willans.py we have the following: