AI-generated test cases can genuinely accelerate test coverage, but understanding what they’re actually good at generating (and what still needs real, deliberate human thought) matters for using them well.
def calculate_shipping_cost(weight_kg, distance_km, is_express):
if weight_kg <= 0:
raise ValueError("Weight must be positive")
base_cost = weight_kg * 2.5 + distance_km * 0.1
return base_cost * 1.5 if is_express else base_cost
# AI-generated tests genuinely tend to be good at covering:
def test_standard_shipping():
assert calculate_shipping_cost(5, 100, False) == 22.5
def test_express_shipping_multiplier():
assert calculate_shipping_cost(5, 100, True) == 33.75
def test_zero_weight_raises_error():
with pytest.raises(ValueError):
calculate_shipping_cost(0, 100, False)
def test_negative_weight_raises_error():
with pytest.raises(ValueError):
calculate_shipping_cost(-1, 100, False)
What AI-Generated Tests Genuinely Excel At
- Systematically covering explicit boundary conditions visible directly in the code (zero, negative, and other obviously edge-case values)
- Generating a genuinely comprehensive combinatorial set of straightforward input variations quickly
- Catching real regressions in already-well-specified, well-understood, mechanical logic
What Still Genuinely Needs Human Judgment
- Tests genuinely reflecting real business requirements not fully, explicitly visible in the code itself (e.g., a specific, real regulatory shipping weight limit that exists but isn’t encoded anywhere in the function’s own logic)
- Deciding which specific tests actually matter most, versus which are genuinely low-value, redundant busy-work that doesn’t meaningfully add coverage
- Integration and true end-to-end scenarios genuinely requiring a real, holistic understanding of the complete system, not just one isolated function in isolation