Testing Three Sum Algorithm Implementation in AutoGPT
This test suite validates the three_sum algorithm implementation, which finds three numbers in an array that sum to a target value. The suite includes comprehensive test cases covering basic scenarios, edge cases with zeros, and operations with negative numbers.
Test Coverage Overview
Implementation Analysis
Technical Details
Best Practices Demonstrated
significant-gravitas/autogpt
classic/benchmark/agbenchmark/challenges/verticals/code/1_three_sum/custom_python/test.py
# pyright: reportMissingImports=false
from typing import List
from sample_code import three_sum
def test_three_sum(nums: List[int], target: int, expected_result: List[int]) -> None:
result = three_sum(nums, target)
print(result)
assert (
result == expected_result
), f"AssertionError: Expected the output to be {expected_result}"
if __name__ == "__main__":
# test the trivial case with the first three numbers
nums = [2, 7, 11, 15]
target = 20
expected_result = [0, 1, 2]
test_three_sum(nums, target, expected_result)
# test for ability to use zero and the same number twice
nums = [2, 7, 0, 15, 12, 0]
target = 2
expected_result = [0, 2, 5]
test_three_sum(nums, target, expected_result)
# test for first and last index usage and negative numbers
nums = [-6, 7, 11, 4]
target = 9
expected_result = [0, 2, 3]
test_three_sum(nums, target, expected_result)