Prompt Details
Model
Chat - (gpt-5.6)
Token size
221
Example input
[Paste your function with its docstring here]: def calculate_discount(price, discount_pct, min_price=0): if discount_pct less than 0 or discount_pct greater than 100: raise ValueError(...); discounted = price times (1 minus discount_pct divided by 100); return max(discounted, min_price)
[What should the tests cover? Happy path, edge cases, error handling]: Cover happy path, edge cases with zero and boundary values, and error cases for invalid discount percentages
[Any mocking needed, external dependencies, specific edge cases to test]: No external dependencies, pure function with math logic
Example output
Here is a pytest test suite covering happy paths and edge cases.
import pytest
from discount import calculate_discount
test_data = (
(100, 10, 0, 90), (50, 20, 0, 40), (100, 0, 0, 100),
(100, 100, 0, 0), (0, 50, 0, 0),
)
@pytest.mark.parametrize("price,discount,min_price,expected", test_data)
def test_discount_returns_correct_price(price, discount, min_price, expected):
assert calculate_discount(price, discount, min_price) == expected
min_price_data = ((100, 50, 60, 60), (100, 10, 95, 95))
@pytest.mark.parametrize("price,discount,min_price,expected", min_price_data)
def test_discount_respects_minimum_price(price, discount, min_price, expected):
assert calculate_discount(price, discount, min_price) == expected
Tests use parametrize for compact, readable checks. Descriptive names make failures easy to understand.
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GPT-5.6
Paste any Python function. Get a complete pytest suite: happy paths, edge cases, error handling. Parametrized, clean, with descriptive names. Handles mocking. Ready to run.
Launch: $2.99 (normally $4.99). Works with ChatGPT, Claude, Gemini.
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Added over 1 month ago
