AI-Assisted Testing
AI coding assistants excel at generating tests. They can quickly produce test scaffolding, suggest edge cases you might miss, and help achieve better coverage. However, AI-generated tests require careful review — they can look comprehensive while testing the wrong things entirely.
Generating Tests With AI
When asking AI to write tests, provide context about what matters:
Write unit tests for this function:
[paste function]
Include:
- Happy path tests
- Edge cases (empty input, null values)
- Error cases (invalid input)
- Boundary conditions
For integration tests, be specific about the testing framework and what interactions matter:
Generate integration tests for this API endpoint:
[paste endpoint code]
Use pytest and cover:
- Success cases with valid data
- Validation errors (400 responses)
- Authentication failures (401 responses)
- Database interaction verification
Finding Missing Test Cases
AI is particularly good at identifying gaps in existing test coverage:
What test cases am I missing for this function:
[paste function]
[paste existing tests]
Consider edge cases, error conditions, and boundary values.
This prompt often reveals scenarios you hadn't considered — empty strings, negative numbers, concurrent access, or unusual input combinations.
Reviewing AI-Generated Tests
AI tests require critical review. Ask yourself:
Do they test the right thing? AI might test that a function returns something rather than the correct thing.
# AI might generate this
def test_calculate_total():
result = calculate_total([10, 20, 30])
assert result is not None # Weak assertion!
# You want this
def test_calculate_total():
result = calculate_total([10, 20, 30])
assert result == 60 # Specific assertion
Are assertions meaningful? Watch for tests that always pass regardless of implementation.
Are edge cases actually covered? AI might claim to test edge cases but use normal values.
Will they catch regressions? If the code breaks, will these tests fail?
Iterating With AI
Use AI iteratively to improve tests:
These tests pass but seem weak. How can I make
the assertions more specific and meaningful?
[paste tests]
Add tests for concurrent access to this function.
What race conditions might occur?
The Human Role
AI accelerates test writing, but you remain responsible for test quality. Use AI to generate the initial structure, then refine assertions, add domain-specific cases, and ensure tests actually validate important behavior.