For every algorithmic change (and every public function/class), require automated tests that lock in both correctness and failure modes.

Minimum standard:

• Prefer assertions/doctests over “print and eyeball.” (Printing-only checks are not sufficient.)
• Add doctests or pytest cases for:
  • happy path(s)
  • edge cases (empty input, boundaries, negative values, single element)
  • invalid inputs / rejected preconditions (at least one case that should fail or return a sentinel)
• Don’t weaken confidence with tests that all assert the same output for different inputs—add cases that meaningfully differ.
• Keep existing tests; don’t remove doctests “just because.”
• Ensure doctests actually execute in the module (e.g., call doctest.testmod() in __main__).
• Avoid nested helper functions when you rely on doctest coverage inside their docstrings; keep testable code at module level.

Example pattern (doctest with edge + failure):

def interpolation_search(sorted_collection: list[int], item: int) -> int | None:
    """
    >>> interpolation_search([1, 2, 3, 4, 5], 2)
    1
    >>> interpolation_search([1, 2, 3, 4, 5], 6) is None
    True
    >>> interpolation_search([], 1) is None
    True
    """
    ...

if __name__ == "__main__":
    import doctest
    doctest.testmod()

This standard directly prevents regressions (new validation rules, edge-case fixes, and invariants) and makes behavior verification repeatable and reviewable.