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Validating AI Test Case Processing in AlphaCodium

This test suite implements validation logic for AI-generated test cases in the AlphaCodium project. It handles the processing and verification of AI-generated test inputs and outputs, with built-in retry mechanisms and error handling.

Test Coverage Overview

The test coverage focuses on the validation workflow for AI-generated test cases.

Key functionality includes:

Test case validation and parsing
YAML configuration loading
Error handling and retry logic
Input/output string formatting

Integration points include the AI inference system and YAML processing components.

Implementation Analysis

The testing approach implements asynchronous execution with error handling wrapped in a retry mechanism. The implementation utilizes Python’s asyncio patterns and functional programming through partial function application. Key technical patterns include:

Async/await pattern for AI inference calls
Functional composition with partial application
YAML parsing and validation
Structured logging implementation

Technical Details

Testing tools and configuration:

Python’s built-in logging framework
Custom AI invoker module
YAML processing utilities
Asynchronous execution framework
Error handling with retry counter

Best Practices Demonstrated

The test implementation demonstrates several quality engineering practices:

Proper error handling and logging
Retry mechanism for resilience
Clean separation of concerns
Modular function composition
Consistent string formatting
Structured logging implementation

codium-ai/alphacodium

alpha_codium/gen/stages/indirect/run_validate_ai_test.py

            
import functools
import logging

from alpha_codium.gen.utils import load_yaml
from alpha_codium.llm.ai_invoker import send_inference
from alpha_codium.log import get_logger

logger = get_logger(__name__)


async def run_validate_ai_tests(self, problem):
    counter_retry = 0
    while True:
        try:
            logger.info("--validate ai tests stage--")

            f = functools.partial(self._run, problem=problem, prompt="code_contests_prompts_validate_ai_tests")
            response_problem_tests, _ = await send_inference(f)
            problem['problem_ai_tests'] = load_yaml(response_problem_tests,
                                        keys_fix_yaml=["input:", "output:", "explanation", "what_was_wrong:"])['tests']

            # clean up and parse the response
            for p in problem['problem_ai_tests']:
                p['input'] = str(p['input']).replace('\
', '
')
                p['output'] = str(p['output']).replace('\
', '
')

            return problem
        except Exception as e:
            logging.error(f"'validate ai tests' stage, counter_retry {counter_retry}, Error: {e}")
            counter_retry += 1
            if counter_retry > 2:
                # raise e
                return problem