#testing

Frameworks and metrics for measuring AI system performance, quality, and safety, from standard benchmarks to domain-specific evaluations.

Discipline of experimenting on production systems to discover weaknesses before they cause incidents, by injecting controlled failures.

Continuous Integration and Continuous Delivery/Deployment — practices that automate code integration, testing, and delivery to production. Foundation of modern software engineering.

Practices, tools, and metrics for maintaining readable, maintainable, testable, and defect-free code over time.

Architectural pattern isolating business logic from the outside world through ports and adapters, facilitating testing and technology changes.

Python project example with Pipenv, Pytest, pre-commit hooks, CI/CD with GitHub Actions, and badge generation.

Approaches and testing levels for validating software works correctly, from unit tests to end-to-end tests and testing in production.