Python for Testing 102 (PT102)

Understand the fundamentals of software testing, learn how different testing techniques fit into the development process, and prepare for the PCAT™ – Certified Associate Tester with Python certification.

Python for Testing 102 (PT102) takes you beyond the fundamentals and into the practical world of building clean, maintainable, and scalable test suites using Python. You’ll learn how professional testers structure their code, manage resources, design robust assertions, apply mocking and fixtures, work with both unittest and pytest, and use TDD/BDD workflows to drive development with clarity and confidence.

By completing this course, you’ll build a strong foundation in software testing and be prepared for the PCAT™ – Certified Associate Tester with Python certification.

Key skills you will learn

Enroll in Python for Testing 102 (PT102) and gain practical skills in:

• Clean test infrastructure using context managers, decorators, and method patterns.
• Unit testing with unittest and advanced assertion techniques.
• Project-driven test development with refactoring, error handling, and edge-case design.
• Fixtures, parameterization, test doubles, and mocks for reliable and maintainable tests.
• Effective use of pytest for scalable, expressive testing workflows.
• Test-Driven Development (TDD) and Behavior-Driven Development (BDD) using Gherkin and step definitions.

Prerequisites

To get the most out of PT102, you should have:

• completed Python for Testing 101 (PT101), or
• the PCEP™ – Certified Entry-Level Python Programmer certification, or
• equivalent foundational Python programming experience.

You should already be comfortable with basic Python programming. We recommend completing Python for Testing 101 (PT101), holding the PCEP™ – Certified Entry-Level Python Programmer certification, or having equivalent experience.

What You Will Learn

By the end of this course, you will be able to:

• Grow a single project end-to-end – continue the Crossword Helper and evolve it (palindromes → ROT shifts → anagrams → pattern matching) with tests that guide the design.
• Strengthen your unittest toolkit – apply test anatomy and F.I.R.S.T., write precise assertions (assertEqual, assertAlmostEqual, assertRaises, assertCountEqual), remove duplication with setUp/tearDown and class/module hooks, use subTest(), and control runs with @skip/@expectedFailure.
• Adopt pytest for speed and clarity – native assert with rich introspection, reusable fixtures (including yield cleanup) and scopes (function → session), parameterization with @pytest.mark.parametrize, markers (skip/xfail/importorskip), targeted selection (-k/-m), verbosity and reports (-v, -r), and centralized config in pytest.ini.
• Mock with confidence – replace slow/fragile I/O using unittest.mock (stubs/fakes/spies, Mock/MagicMock, patch with spec/create_autospec) and the pytest-mock mocker fixture ("mock where it’s used").
• Measure and report – generate coverage with pytest-cov and share results via pytest-html (including self-contained reports).
• TDD & BDD – practice Red → Green → Refactor on small slices (and refactor safely with Pythonic improvements like zip()), then describe behavior with Gherkin (Feature/Scenario, Given/When/Then) and bind to Python step definitions (e.g., behave).