Test Oracle Generator

Generate automated test oracles to verify correct software behavior across multiple oracle types.

Oracle Types

1. Assertion-based: Compare actual vs expected output
2. Property-based: Verify invariants that should always hold
3. Differential: Compare new implementation against reference
4. Metamorphic: Test input-output transformations

Workflow

Step 1: Analyze the Function Under Test

Understand what the function does and its expected behavior.

Checklist:

• Read function signature and docstring
• Identify input parameters and types
• Identify return type and possible values
• Note preconditions and postconditions
• Check for edge cases (empty input, null, boundary values)

Example Analysis:

# Python
def calculate_discount(price: float, discount_percent: int) -> float:
    """Calculate discounted price.

    Args:
        price: Original price (must be positive)
        discount_percent: Discount percentage (0-100)

    Returns:
        Discounted price
    """
    return price * (1 - discount_percent / 100)

Analysis:

• Inputs: price (float, must be > 0), discount_percent (int, 0-100)
• Output: float (discounted price)
• Invariants: Result should be ≤ original price, result should be ≥ 0
• Edge cases: 0% discount, 100% discount, boundary values

Step 2: Generate Assertion-Based Oracles

Create explicit expected value assertions for common cases.

Template:

# Python (pytest)
def test_<function>_<scenario>():
    # Arrange
    input1 = <value>
    input2 = <value>
    expected = <calculated_expected_value>

    # Act
    actual = function_under_test(input1, input2)

    # Assert
    assert actual == expected, f"Expected {expected}, got {actual}"

// Java (JUnit)
@Test
public void test<Function><Scenario>() {
    // Arrange
    Type input1 = <value>;
    Type input2 = <value>;
    Type expected = <calculated_expected_value>;

    // Act
    Type actual = functionUnderTest(input1, input2);

    // Assert
    assertEquals(expected, actual, "Expected and actual should match");
}

Example:

def test_calculate_discount_50_percent():
    # Arrange
    price = 100.0
    discount = 50
    expected = 50.0

    # Act
    actual = calculate_discount(price, discount)

    # Assert
    assert actual == expected
    assert abs(actual - expected) < 0.01  # For floating point

Generate oracles for:

• ✓ Typical cases (middle of valid range)
• ✓ Boundary values (min/max)
• ✓ Edge cases (empty, zero, null)
• ✓ Special values (negative, infinity for numeric types)

Step 3: Generate Property-Based Oracles

Identify invariants and properties that should always hold.

Common Properties:

1. Range properties: Output within expected range
2. Relationship properties: Output relates to input in specific way
3. Conservation properties: Something is preserved (e.g., list length)
4. Idempotence: Applying function twice gives same result as once
5. Commutativity: Order of inputs doesn't matter
6. Associativity: Grouping doesn't matter

Template (Python with hypothesis):

from hypothesis import given, strategies as st

@given(st.floats(min_value=0.01, max_value=10000),
       st.integers(min_value=0, max_value=100))
def test_discount_properties(price, discount_percent):
    result = calculate_discount(price, discount_percent)

    # Property: Result should never exceed original price
    assert result <= price, "Discount should not increase price"

    # Property: Result should be non-negative
    assert result >= 0, "Price cannot be negative"

    # Property: 0% discount returns original price
    if discount_percent == 0:
        assert abs(result - price) < 0.01

    # Property: 100% discount returns 0
    if discount_percent == 100:
        assert abs(result) < 0.01

Template (Java with JUnit Theories):

@Theory
public void discountProperties(
    @ForAll @InRange(min = "0.01", max = "10000") double price,
    @ForAll @InRange(min = "0", max = "100") int discountPercent) {

    double result = calculateDiscount(price, discountPercent);

    // Property: Result should never exceed original price
    assertTrue(result <= price, "Discount should not increase price");

    // Property: Result should be non-negative
    assertTrue(result >= 0, "Price cannot be negative");
}

Identify properties by asking:

• What can I say about the output without computing it exactly?
• What relationships must hold between input and output?
• What constraints must the output satisfy?
• What should never happen?

For detailed property patterns, see references/property_patterns.md.

Step 4: Generate Differential Oracles

Compare new implementation against reference implementation.

Use Cases:

• Refactoring: New optimized version vs old version
• Migration: New library/language vs legacy system
• Bug fixes: Patched version vs unpatched version

Template:

# Python
def test_new_vs_legacy_implementation():
    # Test data
    test_cases = [
        (100.0, 10),
        (50.0, 25),
        (200.0, 0),
        (75.0, 100),
    ]

    for price, discount in test_cases:
        # Compare outputs
        legacy_result = legacy_calculate_discount(price, discount)
        new_result = calculate_discount(price, discount)

        assert abs(legacy_result - new_result) < 0.01, \
            f"Mismatch for ({price}, {discount}): " \
            f"legacy={legacy_result}, new={new_result}"

// Java
@Test
public void testNewVsLegacyImplementation() {
    Object[][] testCases = {
        {100.0, 10},
        {50.0, 25},
        {200.0, 0},
        {75.0, 100}
    };

    for (Object[] testCase : testCases) {
        double price = (double) testCase[0];
        int discount = (int) testCase[1];

        double legacyResult = LegacyClass.calculateDiscount(price, discount);
        double newResult = calculateDiscount(price, discount);

        assertEquals(legacyResult, newResult, 0.01,
            String.format("Mismatch for (%f, %d)", price, discount));
    }
}

Best Practices:

• Generate diverse test data (random, boundary, edge cases)
• Include both typical and unusual inputs
• Log differences for debugging
• Consider performance differences acceptable

Step 5: Generate Metamorphic Oracles

Create test pairs where input transformation produces predictable output transformation.

Metamorphic Relations:

1. Additive: f(x) + f(y) = f(x + y)
2. Multiplicative: f(k × x) = k × f(x)
3. Permutation: f(permute(x)) = permute(f(x))
4. Subset: f(subset(x)) ⊆ f(x)
5. Inverse: f(f⁻¹(x)) = x

Example for discount function:

# Python
def test_discount_metamorphic_double_price():
    """If price doubles, discount amount doubles."""
    price = 100.0
    discount_percent = 20

    result1 = calculate_discount(price, discount_percent)
    result2 = calculate_discount(price * 2, discount_percent)

    discount_amount1 = price - result1
    discount_amount2 = (price * 2) - result2

    # Metamorphic relation: doubling price doubles discount amount
    assert abs(discount_amount2 - 2 * discount_amount1) < 0.01

def test_discount_metamorphic_additive():
    """Applying discount to sum equals sum of individual discounts."""
    price1 = 50.0
    price2 = 30.0
    discount_percent = 15

    # Method 1: Discount on combined price
    combined_result = calculate_discount(price1 + price2, discount_percent)

    # Method 2: Sum of individual discounts
    individual_sum = (calculate_discount(price1, discount_percent) +
                      calculate_discount(price2, discount_percent))

    # Metamorphic relation: Should be equivalent
    assert abs(combined_result - individual_sum) < 0.01

Example for sorting function:

def test_sort_metamorphic_reverse():
    """Reversing then sorting gives same result as sorting."""
    input_list = [3, 1, 4, 1, 5, 9, 2, 6]

    result1 = sort(input_list)
    result2 = sort(list(reversed(input_list)))

    assert result1 == result2

def test_sort_metamorphic_duplicate():
    """Sorting list with duplicated elements maintains order."""
    input_list = [3, 1, 4]
    duplicated = input_list + input_list

    result = sort(duplicated)

    # Should be sorted version of original, doubled
    expected = sorted(input_list) + sorted(input_list)
    assert result == sorted(expected)

For more metamorphic relation patterns, see references/metamorphic_patterns.md.

Step 6: Combine Oracles for Comprehensive Testing

Use multiple oracle types together for robust verification.

Example: Complete test suite for calculate_discount:

import pytest
from hypothesis import given, strategies as st

# Assertion-based oracles
class TestDiscountAssertions:
    def test_50_percent_discount(self):
        assert calculate_discount(100.0, 50) == 50.0

    def test_no_discount(self):
        assert calculate_discount(100.0, 0) == 100.0

    def test_full_discount(self):
        assert calculate_discount(100.0, 100) == 0.0

# Property-based oracles
class TestDiscountProperties:
    @given(st.floats(min_value=0.01, max_value=10000),
           st.integers(min_value=0, max_value=100))
    def test_result_within_bounds(self, price, discount):
        result = calculate_discount(price, discount)
        assert 0 <= result <= price

    @given(st.floats(min_value=0.01, max_value=10000),
           st.integers(min_value=0, max_value=100))
    def test_monotonic_in_discount(self, price, discount):
        """Higher discount percentage means lower price."""
        if discount < 100:
            result1 = calculate_discount(price, discount)
            result2 = calculate_discount(price, discount + 1)
            assert result2 <= result1

# Differential oracles
class TestDiscountDifferential:
    @pytest.mark.parametrize("price,discount", [
        (100.0, 10), (50.0, 25), (200.0, 50)
    ])
    def test_vs_manual_calculation(self, price, discount):
        result = calculate_discount(price, discount)
        expected = price * (1 - discount / 100)
        assert abs(result - expected) < 0.01

# Metamorphic oracles
class TestDiscountMetamorphic:
    def test_double_price_doubles_discount_amount(self):
        price = 100.0
        discount = 20

        discount_amt1 = price - calculate_discount(price, discount)
        discount_amt2 = (price * 2) - calculate_discount(price * 2, discount)

        assert abs(discount_amt2 - 2 * discount_amt1) < 0.01

Step 7: Document and Validate Oracles

Ensure oracles are correct and well-documented.

Oracle Documentation Template:

def test_function_oracle_type():
    """Brief description of what this oracle verifies.

    Oracle Type: [Assertion-based|Property-based|Differential|Metamorphic]

    Rationale: Explain why this property/assertion should hold.

    Edge Cases Covered:
    - Case 1
    - Case 2
    """
    # Test implementation
    pass

Validation Checklist:

• Oracle passes on correct implementation
• Oracle fails on intentionally broken implementation (mutation testing)
• Oracle is deterministic (same input → same result)
• Oracle is independent (doesn't rely on other test state)
• Oracle has clear failure messages
• Oracle is documented with rationale

Mutation Testing (validate oracle effectiveness):

# Introduce deliberate bug to verify oracle catches it
def calculate_discount_buggy(price, discount_percent):
    # BUG: Wrong formula
    return price * discount_percent / 100  # Should be: price * (1 - discount_percent / 100)

# Oracle should fail on buggy version
def test_oracle_detects_bug():
    """Verify oracle catches the bug."""
    with pytest.raises(AssertionError):
        assert calculate_discount_buggy(100, 50) == 50.0  # This should fail

Oracle Selection Guide

Choose Assertion-based when:

• Expected output is easily computable
• Testing specific known scenarios
• Regression testing with saved examples

Choose Property-based when:

• Expected output is hard to compute but properties are clear
• Want to test many random inputs
• Testing invariants that should always hold

Choose Differential when:

• Refactoring or optimizing existing code
• Migrating to new implementation
• Reference implementation exists

Choose Metamorphic when:

• Expected output is unknown or hard to compute
• No reference implementation available
• Want to test complex transformations

Tips

1. Start simple: Begin with assertion-based oracles, add others as needed
2. Combine oracles: Use multiple types for robust verification
3. Test the tests: Use mutation testing to validate oracle effectiveness
4. Document assumptions: Explain why properties should hold
5. Handle floating point: Use tolerance for float comparisons
6. Consider performance: Property-based tests run many iterations
7. Focus on important properties: Not every function needs all oracle types

Common Patterns

For detailed oracle patterns organized by domain, see:

• references/property_patterns.md - Common property-based oracle patterns
• references/metamorphic_patterns.md - Metamorphic relation catalog