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Test

As explained in Learn AI-Assisted Python Programming, Second Edition

A test case is a defined software-testing scenario consisting of inputs and the expected behavior or result.

A test case defines a function call, its input, and the behavior or result that should follow. Writing the expected result before running the code makes the specification concrete and gives you a way to check whether the implementation works. section 6.2.1

Build useful test cases by covering:

  • Common cases, such as ordinary inputs and a single-item list. section 6.2.1
  • Edge cases, such as tied longest words, empty strings, boundary values, negative numbers, or data at the beginning and end of a list. section 6.2.1 section 6.2.2
  • Cases derived from the function’s rules: test both what the function normally does and special behavior it promises, such as returning the first tied result. section 6.2.2
  • A manageable range of parameter values rather than every possible list size or numeric combination. section 6.2.2

Testing a longest-word function

A test case such as longest_word(['a', 'bb', 'ccc']) → 'ccc' checks an ordinary input. Additional cases can check a tie, a one-word list, and a list of empty strings. If a test fails, revise the code or its specification, then rerun the earlier tests as well as the new one; fixing one case can break another. section 6.2.1 section 6.3.1

>>> longest_word(['a', 'bb', 'ccc'])
'ccc'

Shows the compact Python prompt form of a test case: an input call followed by its expected result. section 6.2.1

How Learn AI-Assisted Python Programming, Second Edition covers it

How deep it goes
Explains common and edge-case selection, regression testing, artificial files, and doctest execution without formal coverage methods.
Examples
Demonstrates longest-word lists, classroom rows, NFL passing-yard files, boundary comparisons, and consecutive-student counts.
What it assumes
Assumes a function’s intended inputs and outputs, while teaching how to derive test categories from parameter types and behavior.
What you can do after
Diagnose incorrect behavior, revise code or prompts, and verify common and edge cases with Python doctests or interactive calls.
How it connects
Connects test cases with functions, edge cases, debugging, doctest, prompt engineering, and closed-box testing.

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This explainer was generated by Manning's knowledge graph from the text of Learn AI-Assisted Python Programming, Second Edition. The section links point to the parts of the book it draws on.