coverage-enhancer

Coverage Enhancer

Analyze existing tests and source code to identify coverage gaps, then suggest specific additional tests to improve overall test coverage and code quality.

Core Capabilities

1. Coverage Gap Analysis

Identify untested areas in source code:

• Uncovered lines - Code never executed by tests
• Uncovered branches - Conditional paths not tested
• Uncovered functions - Methods/functions without tests
• Missing error handling tests - Exception paths not verified
• Untested edge cases - Boundary conditions not covered
• Insufficient scenarios - Limited test diversity

2. Existing Test Analysis

Understand current test coverage by:

• Parsing existing test files
• Identifying tested functions and methods
• Recognizing test patterns and frameworks
• Detecting coverage tools in use
• Analyzing test quality and completeness

3. Test Suggestion Generation

Generate specific, actionable test recommendations:

• Complete test code in the project's framework
• Clear test names describing what's being tested
• Setup, execution, and assertion steps
• Integration with existing test structure
• Prioritized by coverage impact

Coverage Analysis Workflow

Step 1: Analyze Existing Tests

Read and understand the current test suite:

Identify test framework:

# pytest
def test_something():
    assert result == expected

# unittest
class TestSomething(unittest.TestCase):
    def test_method(self):
        self.assertEqual(result, expected)

Map tested functionality:

• Which functions/methods have tests?
• What scenarios are covered?
• What assertions are made?
• What inputs are tested?

Identify test patterns:

• Naming conventions
• Setup/teardown patterns
• Fixture usage
• Mock/stub patterns

Step 2: Analyze Source Code

Examine the implementation to find gaps:

Identify code paths:

def process(value):
    if value < 0:        # Branch 1
        raise ValueError
    elif value == 0:     # Branch 2
        return None
    else:                # Branch 3
        return value * 2