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

importosfromdotenvimportload_dotenv# Load API keys from .env fileload_dotenv()
# Set OpenAI API keyos.environ['OPENAI_API_KEY'] ='your-openai-api-key'

Direct Configuration

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

🔬 Detailed Component Breakdown

1. CodeSpec (Specification Model)

CodeSpec(
description: str, # Human-readable description of the codeexpected_inputs: Dict[str, type], # Input type constraintsexpected_outputs: Dict[str, type], # Output type constraintsexample_test_cases: List[Dict[str, Any]], # Predefined test casesconstraints: 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

fromcode_testing_agentimportCodeTester

# Configure with custom OpenAI modeltester=CodeTester(
llm_api_key='your-key',
model='gpt-4', # Specify modeltemperature=0.7, # Adjust creativitymax_tokens=500# Limit response length
)

Extending Analysis

fromcode_testing_agent.analyzersimportCodeAnalyzerclassCustomCodeAnalyzer(CodeAnalyzer):
defadditional_checks(self, tree: ast.AST):
# Add custom static analysis rulespass

🛠 Error Handling

Custom Exceptions

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

try:
result=tester.test_code(code, spec)
exceptValidationErrorase:
print(f"Code validation failed: {e}")
exceptTestExecutionErrorase:
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