The correct option is Generate a Python unit test for the function apply_coupon(total_price, percent_off) that covers normal values, invalid inputs, and boundary conditions.

This prompt clearly states the language and the exact function signature and the categories of tests that must be included. That level of specificity lets Copilot reliably scaffold a complete test suite that exercises typical cases, rejects or handles invalid inputs, and checks edge boundaries. The clarity reduces ambiguity and increases the chance that the generated tests map to the intended behavior of the function.

Including the function name and parameters helps Copilot infer how to call the code and structure assertions. Calling out normal values, invalid inputs, and boundary conditions directs the tool to produce a balanced set of tests rather than a single happy path. This makes the output more deterministic and useful for immediate integration.

Use GitHub Copilot to create a test function for coupon calculations is too vague because it does not name the function, does not specify Python, and does not require any particular coverage categories. Copilot may return incomplete or generic tests that do not match the team’s needs.

Create a Python function that tests coupon discounts using random values asks for a function rather than a proper unit test and encourages randomness which undermines deterministic and repeatable tests. It also fails to reference the target function and the needed coverage categories.

Write a unit test for the apply_coupon function is closer but still underspecified because it does not name the language or the parameters and it does not require normal, invalid, and boundary cases. Copilot may only produce a simple happy path test.

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