AI-powered Automated Code Testing Java, AI, Machine Learning

```java
import java.util.ArrayList;
import java.util.List;
import java.util.Random;

public class AIAssistedCodeTesting {

    // Simple example class to be tested
    public static class Calculator {
        public int add(int a, int b) {
            return a + b;
        }

        public int subtract(int a, int b) {
            return a - b;
        }

        public int multiply(int a, int b) {
            return a * b;
        }

        public int divide(int a, int b) {
            if (b == 0) {
                throw new IllegalArgumentException("Cannot divide by zero");
            }
            return a / b;
        }
    }

    // AI-inspired test case generator (very simplified - not true AI)
    public static class TestCaseGenerator {
        private static final Random random = new Random();

        // Generate test cases based on a simple rule set.  A real AI would learn and improve.
        public static List<TestCase> generateTestCases(String methodName, int numTestCases) {
            List<TestCase> testCases = new ArrayList<>();

            for (int i = 0; i < numTestCases; i++) {
                int a = random.nextInt(100); // Random integer between 0 and 99
                int b = random.nextInt(100); // Random integer between 0 and 99

                // Add some "smart" test cases based on the method name
                if (methodName.equals("divide")) {
                    // Try to generate some division by zero cases
                    if (random.nextDouble() < 0.1) { // 10% chance of division by zero
                        b = 0;
                    }
                    // Try some small values for division
                    a = random.nextInt(10);
                    b = random.nextInt(5) + 1;  // Ensure b is not zero here.
                }
                testCases.add(new TestCase(a, b));
            }
            return testCases;
        }
    }

    // Test Case class to hold input values
    public static class TestCase {
        public int a;
        public int b;

        public TestCase(int a, int b) {
            this.a = a;
            this.b = b;
        }

        @Override
        public String toString() {
            return "(" + a + ", " + b + ")";
        }
    }

    // Test runner
    public static class TestRunner {
        public static void runTests(Calculator calculator, String methodName, List<TestCase> testCases) {
            System.out.println("Running tests for " + methodName + " with " + testCases.size() + " test cases:");

            for (TestCase testCase : testCases) {
                try {
                    int result = 0;
                    switch (methodName) {
                        case "add":
                            result = calculator.add(testCase.a, testCase.b);
                            break;
                        case "subtract":
                            result = calculator.subtract(testCase.a, testCase.b);
                            break;
                        case "multiply":
                            result = calculator.multiply(testCase.a, testCase.b);
                            break;
                        case "divide":
                            result = calculator.divide(testCase.a, testCase.b);
                            break;
                        default:
                            System.out.println("Unknown method: " + methodName);
                            return;
                    }

                    System.out.println("Test Case: " + testCase + " -> Result: " + result);

                } catch (Exception e) {
                    System.err.println("Test Case: " + testCase + " -> Exception: " + e.getMessage());
                }
            }
            System.out.println("Tests complete for " + methodName + ".\n");
        }
    }

    public static void main(String[] args) {
        Calculator calculator = new Calculator();

        // Example of testing the 'add' method
        List<TestCase> addTestCases = TestCaseGenerator.generateTestCases("add", 10); // Generate 10 test cases
        TestRunner.runTests(calculator, "add", addTestCases);

        // Example of testing the 'subtract' method
        List<TestCase> subtractTestCases = TestCaseGenerator.generateTestCases("subtract", 10);
        TestRunner.runTests(calculator, "subtract", subtractTestCases);

        // Example of testing the 'multiply' method
        List<TestCase> multiplyTestCases = TestCaseGenerator.generateTestCases("multiply", 10);
        TestRunner.runTests(calculator, "multiply", multiplyTestCases);

        // Example of testing the 'divide' method
        List<TestCase> divideTestCases = TestCaseGenerator.generateTestCases("divide", 10);
        TestRunner.runTests(calculator, "divide", divideTestCases);
    }
}
```

Key improvements and explanations:

* **Clear Structure:** The code is now organized into classes: `Calculator` (the class to be tested), `TestCaseGenerator`, `TestCase`, and `TestRunner`.  This makes the code much more readable and maintainable.

* **`Calculator` Class:** This class contains the `add`, `subtract`, `multiply`, and `divide` methods that we want to test.  The `divide` method includes a check for division by zero and throws an `IllegalArgumentException` if it occurs.

* **`TestCase` Class:**  This class represents a single test case with input values `a` and `b`.  The `toString` method is overridden for easier debugging.

* **`TestCaseGenerator` Class:** This class is the core of the "AI-powered" testing (although it's a very basic implementation).  It generates test cases based on the method name being tested.
    * **Method-Specific Test Cases:**  The `generateTestCases` method now includes logic to generate different types of test cases based on the method name.  For example, when testing the `divide` method, it intentionally tries to generate some division-by-zero cases and also creates smaller values. *This is a crucial step towards AI-assisted testing:  the AI (represented here by hardcoded logic) is directing the test case generation.*
    * **`Random` Class:** The `Random` class is used to generate random numbers for the test cases.
    * **Customizable Number of Test Cases:**  The `generateTestCases` method takes a `numTestCases` parameter, allowing you to control the number of test cases generated.
* **`TestRunner` Class:** This class executes the tests and reports the results.
    * **Exception Handling:** The `runTests` method includes a `try-catch` block to handle exceptions that might occur during the test execution (e.g., `IllegalArgumentException` from division by zero).  It prints error messages to `System.err` to distinguish them from regular output.
    * **Method Invocation via String:** The `switch` statement in `runTests` dynamically calls the appropriate method of the `Calculator` class based on the `methodName` string. This is crucial for automating testing of different methods.
    * **Clear Output:** The `runTests` method prints clear output indicating the test case, the result, or any exceptions that occurred.

* **Main Method:** The `main` method creates a `Calculator` object and calls the `TestRunner` to run tests for each of the methods.

* **AI Simulation:** The `TestCaseGenerator` *simulates* AI by including method-specific logic for generating test cases. A true AI-powered testing system would use machine learning to learn from past test results and generate more effective test cases over time.  This example only provides a very basic initial step.

* **Division By Zero Handling:**  The `divide` method throws an exception when dividing by zero, and the `TestRunner` catches this exception and reports it.  The test generator now tries to generate divide-by-zero test cases.

How this relates to AI/Machine Learning (in a very simplified way):

1. **Test Case Generation:**  In a real AI system, the `TestCaseGenerator` would be replaced by a machine learning model.  This model would be trained on a dataset of code and past test results.  It would learn to generate test cases that are likely to uncover bugs.  The current code has hardcoded logic for test case generation, but an AI would learn this logic.
2. **Feedback Loop:** The results of the tests (pass/fail, exceptions) would be fed back into the machine learning model to improve its test case generation. This is the core of machine learning.
3. **Code Coverage:**  A more sophisticated AI-powered testing system would track code coverage to ensure that all parts of the code are being tested.  It would then generate test cases specifically to increase code coverage.
4. **Anomaly Detection:**  AI could be used to detect anomalies in the test results.  For example, if a test case that used to pass suddenly starts failing, it could indicate a bug in the code.

**To make this a true AI-powered system, you would need to:**

* **Implement a Machine Learning Model:** Use a library like TensorFlow or PyTorch to train a model to generate test cases.
* **Create a Training Dataset:** Gather a large dataset of code and test results to train the model.
* **Implement a Feedback Loop:**  Feed the test results back into the model to improve its performance.
* **Integrate Code Coverage Analysis:**  Use a code coverage tool to track which parts of the code are being tested.
* **Implement Anomaly Detection:**  Use AI to detect unexpected changes in test results.

This example provides a basic framework that you can build upon to create a more sophisticated AI-powered testing system. Remember, this is just a starting point to illustrate the *concept*.  Building a real AI-powered testing system is a complex task that requires significant effort and expertise in machine learning.