So I’ve been thinking lately about testing strategies again, and one question came up that I kept circling back to: does every function in my Python codebase need to have a doctest?

If you're writing doctests in Python using Gitpod or VS Code, you don’t need much to get started. Python’s built-in doctest module lets you write examples in your function’s docstring using the familiar >>> prompt, and then run those examples as tests. This is great for small, focused functions or when you want your documentation to double as lightweight tests.

Writing docstrings in Python can feel like a chore — especially with the odd formatting involving triple quotes, >>> signs, and parameter blocks. But clear, standardized docstrings are critical for both readability and maintainability.

If you’re using VSCode (Visual Studio Code), you’re in luck. With a few extensions and configurations, you can make writing professional, PEP 257-compliant docstrings painless.

Testing Python Apps with Pytest and Doctest: A Symbiotic Approach

How to test an app using both pytest and doctest in a complementary (symbiotic) way:

We're constantly striving for higher quality, faster delivery, and closer collaboration. In the realm of API development, where contracts and interactions are paramount, these goals often feel like a constant uphill battle. This is where Behavior-Driven Development (BDD) emerges not just as a testing methodology, but as a powerful paradigm for designing, developing, and validating robust Python API systems.

We've all seen projects spiral when API design is an afterthought. It's a common trap: dive straight into coding, then realize downstream that your API is clunky, inconsistent, and a nightmare to integrate with. This is precisely why the Design-First (API-First) paradigm isn't just a buzzword; it's a critical methodology for building scalable, maintainable, and truly collaborative API systems, especially within the Python ecosystem.

Not only Shift-Left mantra matters​

The mantra of "Shift Left" has echoed through the halls of software development for years now, and for good reason. The core idea is simple yet powerful: move testing, quality, and security considerations as early as possible in the development lifecycle. It's about catching problems when they're small and cheap to fix, not when they're gargantuan headaches in production. But as a lead engineer who's seen a few projects through the trenches, I've come to realize that "Shift Left" isn't the destination; it's the starting point.

To truly build robust, maintainable, and high-quality software, especially in a dynamic language like Python, we need to embrace a set of development paradigms that not only support but enhance the "Shift Left" philosophy. These aren't just buzzwords; they are methodologies that, when adopted, fundamentally change how we approach writing code for the better.

I'm continuing the paradigms circle. The Shift Left paradigm is well-known in the DevOps and QA communities for its philosophy of catching bugs, security issues, and integration problems early in the development lifecycle. But Shift Left is not the only valuable philosophy out there. In fact, in some contexts, other paradigms can be even more effective.

This article introduces several software development paradigms that complement or enhance Shift Left—and may even be more impactful depending on your context.

Writing tests in Python helps ensure that your code works correctly. In this guide, we'll use two popular testing tools: the built-in unittest module and the third-party library pytest. We'll walk through both using examples related to vegetables and AI model names.

As a senior or Python developer, refining your team's pre-commit setup can drastically improve code quality, consistency, and onboarding speed. Below are key tips and techniques to supercharge your pre-commit experience.