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hey @jsugg - that sounds like an interesting use case The nice thing about |
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Basically. my first thoughts on it are something like this. import inspect
from unittest.mock import MagicMock
from typing import Any, Callable, TypeVar, get_type_hints
T = TypeVar('T')
def ai_test(test_type: str = 'unit'):
def decorator(cls: T) -> T:
# Introspect the class to find methods to test
for name, method in inspect.getmembers(cls, inspect.isfunction):
if should_generate_test_for(method):
test_methods = generate_test_methods(name, method, test_type)
for test_name, test_method in test_methods.items():
setattr(cls, test_name, test_method)
return cls
def should_generate_test_for(method: Callable) -> bool:
# Logic to decide if a test should be generated for this method
return not name.startswith('_') and not name.startswith('test_')
def generate_test_methods(name: str, method: Callable, test_type: str) -> dict:
# Generate multiple test methods based on AI analysis
if test_type == 'unit':
return generate_unit_test_methods(name, method)
elif test_type == 'integration':
return generate_integration_test_methods(name, method)
else:
raise ValueError(f"Unknown test type: {test_type}")
def generate_unit_test_methods(name: str, method: Callable) -> dict:
# Generate multiple unit test methods with descriptive names
test_methods = {}
# AI logic to determine the number and type of tests to generate
# Example: For each test scenario, create a test method
for i, scenario in enumerate(ai_determine_scenarios(method)):
test_method_name = f'test_{name}_{scenario["description"]}'
test_methods[test_method_name] = create_unit_test_method(method, scenario)
return test_methods
def create_unit_test_method(method: Callable, scenario: dict) -> Callable:
def test_method(self):
# AI logic to generate test cases and assertions based on the scenario
mocks = {param: MagicMock() for param in get_type_hints(method).keys()}
result = method(self, **mocks)
# Assertions based on AI predictions
assert result == scenario["expected_result"]
return test_method
def generate_integration_test_methods(name: str, method: Callable) -> dict:
# Similar logic for integration tests
pass
def ai_determine_scenarios(method: Callable) -> list:
# AI logic to analyze method complexity and suggest test scenarios
# Placeholder for AI model integration
return [{"description": "basic_scenario", "expected_result": ...}]
return decorator
# Example usage:
@ai_test(test_type='unit')
class MyServiceTest:
# Method implementations |
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It might be a nice exercise to enable the writing of test cases, or who knows maybe entire test suites for the codebase, using @ai_fn
Is it feasible?
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