What Is AI Unit Test Generation: Key Strategies and Tools

Unit testing ensures that individual components of the codebase function as expected. However, writing test scripts for all unit test cases can be time-consuming and prone to human error. To mitigate these challenges, you can use intelligent techniques such as AI unit test generation.

With AI unit test generation, you can streamline the process of writing unit test scripts by automating test creation, reducing manual effort, and enhancing your overall testing process.

What Is AI Unit Test Generation?

AI unit test generation is the process of using artificial intelligence to automatically create unit test scripts for software applications. Instead of manually writing test cases for unit testing, AI tools analyze the test steps and generate unit tests.

Let’s say there is a web application whose login module needs to be tested, and the tester writes a test script as shown below, which is expected to fail:

def test_is_valid_password(self):
    if not any(char.isdigit() for char in password):
        return False
    if not any(char.isalpha() for char in password):
        return False
    if not re.search(r"[!@#$%^&*(),.?\":{}|<>]", password):
        return False
    return True


self.assertFalse(is_valid_password("Harish"))

Since the password doesn’t contain any digits or special characters, the test passes on assertFalse.

The above unit test code requires an understanding of each line of the function, knowledge of using a unit test framework (such as unittest in Python), and principles of unit testing to write code efficiently.

This process takes a lot of time, which can be spent creating new features or resolving pending bugs. Instead, let’s see how much time it takes to generate AI unit tests for the same scenario.