Code Testing Agent Framework

📋 Table of Contents

• Overview
• Key Features
• Installation
• Configuration
• Detailed Component Breakdown
• Advanced Usage
• Error Handling
• Performance Considerations
• Future Roadmap

🚀 Overview

An AI-powered code testing framework that analyzes, tests, and provides feedback on code based on specifications.

✨ Key Features

• Static code analysis using AST
• Automatic test case generation
• AI-powered edge case analysis
• Comprehensive test execution
• Detailed feedback and reporting

🤖 Supported Language Models

Model	Provider	Support Level	Capabilities
GPT-4	OpenAI	Full	Advanced analysis, comprehensive feedback
GPT-3.5 Turbo	OpenAI	Basic	Standard analysis, limited depth
Claude 3 Opus	Anthropic	Experimental	Advanced reasoning, nuanced feedback

💻 Installation

Install from PyPI

pip install code-testing-agent

Or install from source

git clone https://github.com/yourusername/code-testing-agent.git
cd code-testing-agent
pip install -e .

🔧 Configuration

🔑 API Key Management

Environment Variables

import os
from dotenv import load_dotenv

# Load API keys from .env file
load_dotenv()

# Set OpenAI API key
os.environ['OPENAI_API_KEY'] = 'your-openai-api-key'

Direct Configuration

from code_testing_agent import CodeTester

# Initialize with API key
tester = CodeTester(llm_api_key='your-api-key')

🔬 Detailed Component Breakdown

1. CodeSpec (Specification Model)

CodeSpec(
    description: str,  # Human-readable description of the code
    expected_inputs: Dict[str, type],  # Input type constraints
    expected_outputs: Dict[str, type],  # Output type constraints
    example_test_cases: List[Dict[str, Any]],  # Predefined test cases
    constraints: Optional[List[str]] = None  # Additional code constraints
)

Example

spec = CodeSpec(
    description="Calculate average of a list of numbers",
    expected_inputs={"numbers": list},
    expected_outputs={"result": float},
    example_test_cases=[
        {
            "inputs": {"numbers": [1, 2, 3, 4, 5]},
            "expected": 3.0
        }
    ],
    constraints=[
        "Input list must contain only numbers",
        "Handles empty list by returning 0"
    ]
)

2. 🕵️ Advanced Analysis Techniques

Static Code Analysis

• Abstract Syntax Tree (AST) parsing
• Syntax structure evaluation
• Potential issue detection
• Code complexity assessment

Potential Detected Issues

• Mutable default arguments
• Infinite loops
• Redundant comparisons
• Exception handling anti-patterns

🚀 Advanced Usage

Custom Model Configuration

from code_testing_agent import CodeTester

# Configure with custom OpenAI model
tester = CodeTester(
    llm_api_key='your-key',
    model='gpt-4',  # Specify model
    temperature=0.7,  # Adjust creativity
    max_tokens=500   # Limit response length
)

Extending Analysis

from code_testing_agent.analyzers import CodeAnalyzer

class CustomCodeAnalyzer(CodeAnalyzer):
    def additional_checks(self, tree: ast.AST):
        # Add custom static analysis rules
        pass

🛠 Error Handling

Custom Exceptions

CodeTestingError: Base exception ValidationError: Code validation failures TestExecutionError: Test runtime errors

try:
    result = tester.test_code(code, spec)
except ValidationError as e:
    print(f"Code validation failed: {e}")
except TestExecutionError as e:
    print(f"Test execution error: {e}")

📊 Performance Considerations

• Caching analysis results
• Configurable recursion limits
• Timeout mechanisms for long-running tests
• Selective test case execution

🚦 Future Roadmap

• Multi-language support
• More advanced AI models
• Enhanced test case generation
• Performance profiling
• Integration with CI/CD pipelines