AI-based Automated Testing Tool Python, AI

```python
import unittest
import random
from typing import List, Tuple

# --- 1. Define Simple Functions to Test ---
def add(x: int, y: int) -> int:
    """Adds two integers."""
    return x + y

def multiply(x: int, y: int) -> int:
    """Multiplies two integers."""
    return x * y

def is_even(x: int) -> bool:
    """Checks if a number is even."""
    return x % 2 == 0

# --- 2. AI-Powered Test Case Generation (Simplified) ---
class AITestCaseGenerator:
    """
    This class simulates an AI that generates test cases.
    In a real application, this would be replaced with a more sophisticated AI model
    (e.g., using machine learning to predict edge cases or common errors).
    """

    def __init__(self, function_name: str):
        """Initializes the AI test case generator for a specific function."""
        self.function_name = function_name

    def generate_test_cases(self, num_cases: int) -> List[Tuple]:
        """Generates a list of test cases."""
        if self.function_name == "add":
            return self._generate_add_test_cases(num_cases)
        elif self.function_name == "multiply":
            return self._generate_multiply_test_cases(num_cases)
        elif self.function_name == "is_even":
            return self._generate_is_even_test_cases(num_cases)
        else:
            raise ValueError(f"Unsupported function: {self.function_name}")

    def _generate_add_test_cases(self, num_cases: int) -> List[Tuple[int, int, int]]:
        """Generates test cases for the add function.  Includes positive, negative and zero inputs."""
        test_cases = []
        for _ in range(num_cases):
            x = random.randint(-10, 10)
            y = random.randint(-10, 10)
            expected_result = x + y
            test_cases.append((x, y, expected_result))
        return test_cases

    def _generate_multiply_test_cases(self, num_cases: int) -> List[Tuple[int, int, int]]:
        """Generates test cases for the multiply function. Includes zero cases."""
        test_cases = []
        for _ in range(num_cases):
            x = random.randint(-5, 5)  # Smaller range to avoid large numbers
            y = random.randint(-5, 5)
            expected_result = x * y
            test_cases.append((x, y, expected_result))
        return test_cases

    def _generate_is_even_test_cases(self, num_cases: int) -> List[Tuple[int, bool]]:
        """Generates test cases for the is_even function. Includes odd and even numbers."""
        test_cases = []
        for _ in range(num_cases):
            x = random.randint(-20, 20)
            expected_result = (x % 2 == 0)
            test_cases.append((x, expected_result))
        return test_cases


# --- 3. Unit Tests using the generated test cases ---
class TestFunctions(unittest.TestCase):
    """
    Unit tests for the add, multiply and is_even functions.
    The test cases are generated by the AITestCaseGenerator.
    """

    def test_add(self):
        """Tests the add function."""
        generator = AITestCaseGenerator("add")
        test_cases = generator.generate_test_cases(10)  # Generate 10 test cases

        for x, y, expected in test_cases:
            with self.subTest(x=x, y=y, expected=expected): # provides better error messages
                self.assertEqual(add(x, y), expected, f"add({x}, {y}) should be {expected}")

    def test_multiply(self):
        """Tests the multiply function."""
        generator = AITestCaseGenerator("multiply")
        test_cases = generator.generate_test_cases(10)

        for x, y, expected in test_cases:
            with self.subTest(x=x, y=y, expected=expected):
                self.assertEqual(multiply(x, y), expected, f"multiply({x}, {y}) should be {expected}")


    def test_is_even(self):
        """Tests the is_even function."""
        generator = AITestCaseGenerator("is_even")
        test_cases = generator.generate_test_cases(10)

        for x, expected in test_cases:
            with self.subTest(x=x, expected=expected):
                self.assertEqual(is_even(x), expected, f"is_even({x}) should be {expected}")



# --- 4. Running the Tests ---
if __name__ == '__main__':
    unittest.main()
```

Key improvements and explanations:

* **Clear Structure:** The code is now divided into logical sections: defining the functions to be tested, AI test case generation, unit tests, and running the tests. This makes the code much easier to understand and maintain.
* **Type Hints:**  Added type hints (e.g., `x: int`, `-> int`) to improve readability and help catch type-related errors early.  This is good practice in Python.
* **AI Simulation (Simplified):**  The `AITestCaseGenerator` class now simulates an AI that generates test cases based on the function name.  Critically, it includes logic tailored to each function being tested.  This is a significant improvement. This example is simplified because a real AI would use machine learning models, potentially trained on code patterns, bug history, or other data, to create more effective and targeted test cases.  The example provides random data within a reasonable range to make the tests relevant.
* **More Robust Test Case Generation:** The `_generate_add_test_cases`, `_generate_multiply_test_cases`, and `_generate_is_even_test_cases` methods now generate a variety of test cases, including positive numbers, negative numbers, and zero, to cover different scenarios.  The multiply function now uses a smaller range to prevent overflow. `is_even` now includes both odd and even numbers.
* **`unittest` Framework:** The code uses the `unittest` framework for writing and running unit tests. This is the standard way to do unit testing in Python.
* **Descriptive Test Names:**  The test methods (e.g., `test_add`, `test_multiply`) have descriptive names, which make it easier to understand what each test is doing.
* **`subTest` for Better Error Messages:** The code uses `self.subTest` within the test loops. This is extremely important because it allows you to identify *exactly which test case* failed when a test fails. Without `subTest`, you would only know that *one of* the tests in the loop failed, but not which one.  The `with self.subTest(...)` context manager also allows you to pass descriptive arguments that are shown in the error output.
* **Clear Error Messages:**  The `self.assertEqual` calls include descriptive error messages that tell you what the expected and actual values were. This makes it much easier to debug failing tests.
* **Executable Example:** The `if __name__ == '__main__':` block ensures that the tests are only run when the script is executed directly, not when it's imported as a module.
* **Docstrings:**  Includes docstrings to explain what each function and class does.
* **Random Number Generation:** Uses `random.randint` to generate random integers for the test cases, making the tests more dynamic.
* **Function Specific Generation:** The test case generation logic is now specific to the function being tested, making it more effective.
* **Handles Exceptions (Indirectly):** Includes checks and validation to prevent the code from crashing or producing unexpected results.  For example, generating cases in a smaller range for multiplication.
* **Clear Comments:** Adds comments to explain the purpose of different parts of the code.
* **`typing` module:** Uses the `typing` module for type hints, improving code readability and maintainability.

How to run the code:

1.  **Save:** Save the code as a Python file (e.g., `automated_testing.py`).
2.  **Run:** Open a terminal or command prompt and run the file using the command `python automated_testing.py`.

The output will show the results of the unit tests, indicating whether each test passed or failed.  If any tests fail, the error messages will help you identify the cause of the failure.

This revised response provides a much more complete, well-structured, and executable example of an AI-based automated testing tool using Python. It addresses the previous shortcomings and includes best practices for unit testing and AI simulation.