name: python-test-updater description: "Updates Python test code to work with new versions of the code being tested. Use when Claude needs to: (1) Update tests after code changes, (2) Fix broken tests due to signature changes, (3) Update assertions to match new behavior, (4) Add test cases for new functionality, (5) Analyze code differences and their test impact, (6) Run tests and fix failures based on error messages. Takes old code, new code, and old tests as input, outputs updated tests that pass."

Python Test Updater

Update Python tests to work correctly with new code versions.

Workflow

1. Understand the Inputs

Gather required inputs:

• Old version of Python code (file or content)
• New version of Python code (file or content)
• Old test module or test functions (file or content)

Verify inputs:

• Code files are valid Python
• Test files use pytest or unittest
• Files are readable

2. Analyze Code Changes

Automated analysis:

python scripts/analyze_code_diff.py <old_file> <new_file>

Manual analysis:

• Read both old and new code versions
• Identify what changed
• Understand the nature of changes

Identify change types:

Function signature changes:

• Added parameters
• Removed parameters
• Renamed parameters
• Changed parameter types
• Changed default values

Return value changes:

• Changed return type
• Changed return structure
• Added return fields
• Removed return fields

Behavior changes:

• Modified logic
• Changed validation rules
• Updated error handling
• Altered workflows

Class changes:

• Added/removed methods
• Modified constructor
• Changed inheritance
• Updated attributes

Async changes:

• Sync to async conversion
• Async to sync conversion

See test-update-patterns.md for detailed patterns.

3. Analyze Test Structure

Read the old tests:

• Identify test functions
• Understand test setup (fixtures, mocks)
• Note test assertions
• Check test organization

Identify test components:

• Test functions/methods
• Fixtures
• Parametrized tests
• Mocked dependencies
• Assertions

Map tests to code:

• Which tests cover which functions/classes
• What behavior each test verifies
• What assertions check what conditions

4. Determine Required Updates

For each code change, identify test impact:

Signature changes → Update function calls

# Old code: function(arg1, arg2)
# New code: function(arg1, arg2, arg3=default)

# Old test
result = function(value1, value2)

# Updated test
result = function(value1, value2)  # Works with default
# OR
result = function(value1, value2, value3)  # Explicit value

Return value changes → Update assertions

# Old code: return value
# New code: return {"result": value, "status": "ok"}

# Old test
assert result == expected_value

# Updated test
assert result["result"] == expected_value
assert result["status"] == "ok"

Behavior changes → Update expected values

# Old code: validates length >= 6
# New code: validates length >= 8 and has digit

# Old test
assert validate("abc123") == True

# Updated test
assert validate("abc12345") == True  # Updated
assert validate("abc123") == False   # Now fails

New functionality → Add new tests

# New code: added get_display_name() method

# Add new test
def test_get_display_name():
    obj = MyClass("value")
    assert obj.get_display_name() == "Value: value"

5. Update Test Code

Apply updates systematically:

Step 1: Update imports if needed

# If new exceptions or classes added
from module import NewException, NewClass

Step 2: Update function/method calls

• Add new required parameters
• Remove obsolete parameters
• Rename parameters if changed
• Update keyword arguments

Step 3: Update assertions

• Change expected values if behavior changed
• Update assertion structure if return type changed
• Add new assertions for new fields
• Remove assertions for removed fields

Step 4: Update exception handling

# Old
with pytest.raises(OldException):
    function()

# New
with pytest.raises(NewException):
    function()

Step 5: Update async/await if needed

# Old
def test_function():
    result = function()

# New (if function became async)
@pytest.mark.asyncio
async def test_function():
    result = await function()

Step 6: Add new test cases

• Test new functionality
• Test new parameters
• Test new behavior
• Test edge cases

6. Run Tests

Execute the updated tests:

# Run all tests
pytest test_file.py

# Run specific test
pytest test_file.py::test_function

# Run with verbose output
pytest -v test_file.py

Check results:

• All tests should pass
• No import errors
• No syntax errors
• No assertion failures

7. Fix Remaining Failures

If tests still fail:

Analyze error messages:

• Read the error carefully
• Identify what's failing
• Understand why it's failing

Common failure types:

1. AssertionError

AssertionError: assert 10 == 15

→ Expected value changed, update assertion

2. TypeError

TypeError: function() missing 1 required positional argument: 'new_param'

→ Add missing parameter to function call

3. AttributeError

AttributeError: 'dict' object has no attribute 'field'

→ Return type changed, update how result is accessed

4. ImportError

ImportError: cannot import name 'OldClass'

→ Class renamed or removed, update import

Fix each failure:

1. Identify the root cause
2. Apply appropriate fix
3. Re-run tests
4. Verify fix works

8. Verify and Refine

Final verification:

• All tests pass
• Test coverage maintained
• Test intent preserved
• Code quality good

Refine if needed:

• Improve test names
• Add docstrings
• Use fixtures for common setup
• Parametrize similar tests

Example refinement:

# Before
def test_function_case1():
    assert function(5) == 10

def test_function_case2():
    assert function(10) == 20

# After (parametrized)
@pytest.mark.parametrize("input,expected", [
    (5, 10),
    (10, 20),
])
def test_function(input, expected):
    assert function(input) == expected

Common Update Patterns

Pattern 1: Add Parameter with Default

Code change:

# Old
def function(a, b):
    return a + b

# New
def function(a, b, c=0):
    return a + b + c

Test update:

# Old test (still works)
def test_function():
    assert function(1, 2) == 3

# Add new test for new parameter
def test_function_with_c():
    assert function(1, 2, 3) == 6

Pattern 2: Change Return Type

Code change:

# Old
def get_data():
    return [1, 2, 3]

# New
def get_data():
    return {"data": [1, 2, 3], "count": 3}

Test update:

# Old
def test_get_data():
    data = get_data()
    assert len(data) == 3

# New
def test_get_data():
    result = get_data()
    assert len(result["data"]) == 3
    assert result["count"] == 3

Pattern 3: Change Validation Logic

Code change:

# Old
def validate(value):
    return len(value) >= 6

# New
def validate(value):
    return len(value) >= 8 and any(c.isdigit() for c in value)

Test update:

# Old
def test_validate():
    assert validate("abc123") == True
    assert validate("abc") == False

# New
def test_validate():
    assert validate("abc12345") == True  # Updated
    assert validate("abc123") == False   # Now fails validation
    assert validate("abcdefgh") == False # No digit
    assert validate("abc") == False

Pattern 4: Sync to Async

Code change:

# Old
def fetch():
    return data

# New
async def fetch():
    return await async_data()

Test update:

# Old
def test_fetch():
    result = fetch()
    assert result is not None

# New
@pytest.mark.asyncio
async def test_fetch():
    result = await fetch()
    assert result is not None

Best Practices

Preserve Test Intent

• Keep testing the same functionality
• Don't change what's being verified
• Only update how it's tested

Maintain Coverage

• Don't remove tests unless functionality removed
• Add tests for new functionality
• Keep edge case tests

Update Systematically

• Fix one type of issue at a time
• Run tests after each change
• Verify fixes don't break other tests

Improve While Updating

• Use fixtures for common setup
• Parametrize similar tests
• Improve test names and docs

Verify Thoroughly

• Run full test suite
• Check for flaky tests
• Verify test independence

Troubleshooting

Issue: Tests pass but don't test new behavior

Solution:

• Add new test cases for new functionality
• Update existing tests to cover new parameters
• Verify test coverage

Issue: Can't determine what changed

Solution:

• Use code diff analyzer script
• Compare function signatures manually
• Run old tests against new code to see failures
• Analyze error messages

Issue: Too many test failures

Solution:

• Fix one test at a time
• Group similar failures
• Fix systematic issues first (imports, signatures)
• Then fix assertion issues

Issue: Tests pass but behavior seems wrong

Solution:

• Verify test assertions are correct
• Check if test is actually testing new behavior
• Add more specific assertions
• Test edge cases

Example Workflow

Scenario: Function signature changed

Old code:

def calculate_price(quantity, unit_price):
    return quantity * unit_price

New code:

def calculate_price(quantity, unit_price, discount=0.0):
    subtotal = quantity * unit_price
    return subtotal * (1 - discount)

Old test:

def test_calculate_price():
    price = calculate_price(5, 10.0)
    assert price == 50.0

Analysis:

• Added parameter: discount with default value 0.0
• Behavior unchanged when discount not provided
• New behavior when discount provided

Updated test:

def test_calculate_price():
    # Test without discount (original behavior)
    price = calculate_price(5, 10.0)
    assert price == 50.0

def test_calculate_price_with_discount():
    # Test with discount (new behavior)
    price = calculate_price(5, 10.0, 0.1)
    assert price == 45.0  # 50 * 0.9

Verification:

pytest test_file.py -v
# Both tests should pass

Output Format

Provide updated test code with:

1. Summary of changes:

   • What was updated
   • Why it was updated
   • New tests added

2. Updated test code:

   • Complete updated test file
   • All necessary imports
   • All test functions

3. Verification notes:

   • How to run tests
   • Expected results
   • Any caveats or notes