Testing and Software Quality

Working code is only half the job. The other half is knowing it works — today, after next week's change, and after someone else edits it. This track teaches you to write automated tests: small functions that call your code with chosen inputs and check the results, so a bug shows up as a failing test in seconds instead of a complaint from a customer weeks later. You start with plain assert statements, move to pytest (the tool most Python teams use), and learn the techniques that make tests worth having: checking exceptions and floating-point results correctly, hunting for edge cases at boundaries, writing table-driven tests with parametrize, keeping tests independent with fixtures and temporary files, and working test-first.

It is written for learners who have finished the earlier tracks and can write functions, classes and modules. Here the roles flip: in many exercises you write the tests, and the grader checks them by running them against deliberately broken versions of the code. A test that asserts nothing useful will not pass. You also use tests as a safety net to refactor messy code, make code readable with good names, small functions and type hints, see what tools like mypy and ruff catch before you run anything, and finish by debugging a pricing module the professional way: reproduce each bug as a failing test, fix it, and keep the test so the bug never returns.