Test Generation

Test Generation with LLMs

Automated Test Generation
LLMs can generate unit tests, integration tests, and test data based on code analysis.

Unit Test Generation

def generate_tests(function_code: str, language: str) -> str:
    return llm.generate(f"""
Generate comprehensive unit tests for this {language} function.
Include:
- Happy path tests
- Edge cases
- Error handling
- Boundary conditions

Function:

{language}
{function_code}

Generate tests using pytest/unittest:
    """)

## Test Coverage Expansion

def expand_coverage(code: str, existing_tests: str) -> str:
    return llm.generate(f"""
Analyze this code and existing tests.
Generate additional tests to improve coverage.

Code:
{code}

Existing tests:
{existing_tests}

Additional tests needed:
    """)

Property-Based Test Hints

def suggest_properties(function_code: str) -> str:
    return llm.generate(f"""
Suggest property-based tests (hypothesis-style) for this function.
What invariants should hold?

Function:
{function_code}

Properties to test:
    """)

Test Data Generation

def generate_test_data(schema: str, count: int) -> str:
    return llm.generate(f"""
Generate {count} realistic test records matching this schema:
{schema}

Return as JSON array.
    """)

Integration with Testing Frameworks
| Framework | Use Case |
|-----------|----------|
| pytest | Python unit tests |
| Jest | JavaScript testing |
| JUnit | Java testing |
| Hypothesis | Property-based testing |

Workflow Integration

# CI/CD integration
def review_test_coverage(pr_code: str, pr_tests: str) -> dict:
    return llm.generate(f"""
Evaluate test coverage for this PR.

New code:
{pr_code}

New tests:
{pr_tests}

Assess:
- Are all new functions tested?
- Are edge cases covered?
- Any missing test scenarios?
    """)

Limitations
- Generated tests may have bugs
- May not understand complex business logic
- Could miss important edge cases
- Always review generated tests