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How to use AI to generate unit tests

Q: How to use AI to generate unit tests

Use an AI model like gpt-4o to generate unit tests by providing it with your source code and a prompt requesting test cases. The AI returns test code snippets you can integrate into your test suite, automating test creation and improving coverage.

Quick answer

Use an AI model like gpt-4o to generate unit tests by providing it with your source code and a prompt requesting test cases. The AI returns test code snippets you can integrate into your test suite, automating test creation and improving coverage.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the OpenAI Python SDK and set your API key as an environment variable to authenticate requests.

bash

pip install openai>=1.0

Step by step

Provide your source code and a clear prompt to the gpt-4o model to generate unit tests. The example below shows a complete script that sends a Python function and asks the AI to write pytest unit tests for it.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

source_code = '''
def add(a, b):
    """Return the sum of two numbers."""
    return a + b
'''

prompt = f"""Generate pytest unit tests for the following Python function:\n{source_code}\n"""

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": prompt}]
)

print(response.choices[0].message.content)

output

import pytest

from your_module import add

def test_add_positive_numbers():
    assert add(2, 3) == 5

def test_add_negative_numbers():
    assert add(-1, -1) == -2

def test_add_zero():
    assert add(0, 0) == 0

Common variations

Use claude-3-5-sonnet-20241022 for potentially better code generation quality.
Generate tests asynchronously using async SDK calls.
Customize prompts to generate tests for edge cases or specific frameworks like unittest or nose.

python

import os
import asyncio
from openai import OpenAI

async def generate_tests_async():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    source_code = '''\ndef multiply(x, y):\n    return x * y\n'''
    prompt = f"Generate unittest test cases for this function:\n{source_code}\n"

    response = await client.chat.completions.acreate(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    print(response.choices[0].message.content)

asyncio.run(generate_tests_async())

output

import unittest

from your_module import multiply

class TestMultiply(unittest.TestCase):
    def test_multiply_positive(self):
        self.assertEqual(multiply(3, 4), 12)

    def test_multiply_zero(self):
        self.assertEqual(multiply(0, 5), 0)

Troubleshooting

If the AI generates incomplete or incorrect tests, refine your prompt with more context or examples.
Ensure your API key is correctly set in os.environ["OPENAI_API_KEY"] to avoid authentication errors.
Check for rate limits or quota issues if requests fail unexpectedly.

✅

Key Takeaways

Use clear, specific prompts including source code to get accurate unit test generation from AI.
The gpt-4o model is effective for generating Python unit tests with minimal setup.
Async calls and alternative models like claude-3-5-sonnet-20241022 offer flexibility and potentially higher quality.
Always validate AI-generated tests manually before integrating into your CI pipeline.
Proper environment setup and prompt engineering are key to reliable AI test generation.

Verified 2026-04 · gpt-4o, claude-3-5-sonnet-20241022

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