AI Testing
Computer vision models break in ways that unit tests don't catch. Your preprocessing pipeline silently changes a pixel value range, your bounding box coordinates are in the wrong format, or your model returns detections in a different order than your downstream code expects. None of these show up
Python Testing
If you're adding BDD to a Python project, you'll face a choice between Behave and pytest-bdd. They both use Gherkin feature files and step definitions. They're both mature and actively maintained. The differences are architectural, and which one is better depends heavily on
Python Testing
Step definitions are where Gherkin meets Python. Writing them well is the difference between a test suite that's easy to maintain and one that becomes a tangled mess of duplicated code. This post covers everything about step definitions: parameter types, regex matching, the context object, reusing steps, and
Python Testing
Test reports serve two audiences: developers triaging failures and stakeholders tracking quality. Behave's built-in formatters handle the first reasonably well. For the second — and for modern CI pipelines — you need JUnit XML, Allure, or a custom formatter. This post covers the full reporting stack. Built-in Formatters
Python Testing
Behave is the most widely used BDD framework for Python. It lets you write tests in plain English using Gherkin syntax, then back them with Python step definitions. If your team wants living documentation that non-engineers can read and contribute to, Behave is a solid choice. This guide covers
Temporal.io
Temporal.io solves distributed workflow reliability by making long-running processes durable, resumable, and observable.
Prefect
Prefect 2 has a developer-friendly testing story. Flows and tasks are plain Python functions with decorators — you can call them directly in tests, run them with local storage instead of remote infrastructure, and control state management without spinning up Prefect Cloud. This guide covers the full testing surface: task
Apache Airflow
Apache Airflow DAGs fail in production in ways that no amount of YAML linting will catch.