Using AI to Generate Unit Tests for Your Code

Introduction

Generative AI can help you draft unit tests that exercise your code in ways you might not anticipate. By analyzing function signatures, docstrings, and typical edge cases, an AI assistant can propose test cases, scaffolding, and even property-based tests. This post outlines how to adopt AI-driven test generation responsibly, what to expect, and how to integrate it into real-world workflows.

The promise and perils of AI-generated tests

• Promise: Increase test coverage quickly, surface edge cases, and provide a starting point for test suites.
• Peril: AI can produce brittle, flaky, or incorrect tests if prompts are vague or data is biased. Tests might overfit to the prompt rather than the actual behavior, or miss domain-specific invariants.

Key takeaway: use AI as a helper, not a replacement for human judgment. Always review, tailor, and verify generated tests against your domain knowledge and code semantics.

Setting up an AI-driven test generation workflow

• Define clear prompts: Explain the function or module, desired behavior, and any known constraints or invariants.
• Provide code context: Include function signatures, existing public APIs, and sample inputs/outputs.
• Iterate prompts: Start with basic tests, then request edge cases, error handling, and performance-related scenarios.
• Validate output: Run the generated tests, assess flakiness, and refine the prompts accordingly.
• Integrate tooling: Use your favorite AI tool or model in tandem with your test runner (e.g., pytest, Jest, JUnit).

Suggested workflow:

• Step 1: Run AI to generate a first draft of unit tests for a target module.
• Step 2: Review and prune tests, ensuring determinism and alignment with requirements.
• Step 3: Add any domain-specific invariants or performance expectations that AI might miss.
• Step 4: Add CI checks to enforce test generation quality (e.g., PR comments or test coverage gates).

Integrating AI test generation into CI/CD

• Generate tests on PR creation or on a scheduled CI run, producing a test file or patch with suggested tests.
• Require a human review step before merging, focusing on correctness and maintainability.
• Run all tests, including the AI-generated ones, as part of the standard test suite.
• Track metrics: coverage changes, flaky-test rate, and maintenance cost to monitor the impact over time.

Best practices and guardrails

• Start with small, well-scoped components to validate the approach.
• Prioritize determinism: tests should be deterministic and not rely on external state unless properly mocked.
• Include explicit edge cases: empty inputs, boundary values, and error paths.
• Cross-check with property-based testing where applicable to capture invariants beyond example inputs.
• Maintain human review: always sign off on AI-generated tests before integrating them into main branches.
• Document the rationale behind generated tests to aid future maintenance.

A concrete example: generating tests for a small function

Suppose you want AI to help generate tests for a simple is_prime function in Python.

Code under test:

def is_prime(n):
    if n <= 1:
        return False
    if n <= 3:
        return True
    if n % 2 == 0:
        return False
    i = 3
    while i * i <= n:
        if n % i == 0:
            return False
        i += 2
    return True

AI-generated test sketch (illustrative):

def test_is_prime_basic_cases():
    assert is_prime(2) is True
    assert is_prime(3) is True
    assert is_prime(4) is False

def test_is_prime_small_primes():
    for p in [5, 7, 11, 13]:
        assert is_prime(p) is True

def test_is_prime_small_composites():
    for n in [9, 15, 21]:
        assert is_prime(n) is False

def test_is_prime_large_prime():
    assert is_prime(97) is True

def test_is_prime_edge_cases():
    assert is_prime(-5) is False
    assert is_prime(0) is False

What this demonstrates:

• AI can propose a baseline set of tests covering typical cases, edge conditions, and a few larger inputs.
• You should review for correctness, add missing invariants, and ensure tests remain readable and maintainable.

Conclusion

AI-assisted unit test generation can accelerate test creation, surface edge cases, and help maintain robust test suites. Treat AI-generated tests as a starting point: review for correctness, align with domain-specific requirements, and embed them into well-integrated CI workflows. With disciplined prompts and rigorous review, AI can become a valuable ally in sustaining software quality.