Code Quality Improvement Tool

Overview

This tool is designed to analyze and improve the quality of Python code repositories. It uses various linters, static code analysis tools, and AI-powered suggestions to identify and fix potential issues in your codebase.

This code is considered alpha as of 2024-10-23

98% of this code was written by aider

Requirements

• Python 3.12 or higher
• Git
• Virtual environment (recommended)
• SQLite3

Core Components

• agent_v2.py: Main agent for processing Python files
• aider_runner.py: Runs Aider to fix code issues
• command_runner.py: Executes system commands securely
• config.py: Configuration management with validation
• file_processor.py: Processes individual files
• git_manager.py: Handles Git operations
• issue_processor.py: Analyzes and groups code issues
• linter_runner.py: Runs various code linters
• logger.py: Centralized logging system
• resource_manager.py: Manages system resources
• suggestion_db.py: SQLite database management
• suggestion_api.py: FastAPI-based suggestion API
• suggestion_cli.py: CLI interface
• suggestion_web.py: Flask web interface

Features

• Scans Python files in a git repository
• Runs multiple linters (Pylint, Flake8, Ruff)
• Processes and groups issues by type and function
• Uses AI (via Aider) to suggest and apply fixes
• Provides secure web and CLI interfaces
• Supports running tests to validate fixes
• Allows interrogation of specific files using AI
• Resource monitoring and management
• Rate limiting and error handling
• Secure input validation and sanitization

Installation

1. Install aider-chat:

   pip install aider-chat==0.60.0

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   • For local development:

     pip install -r requirements.txt

   • For Docker deployment:

     pip install -r requirements.docker.txt

Configuration

Local Development

cp config_example.yaml config_local.yaml

Docker Deployment

cp config_example.yaml config_docker.yaml

Key Configuration Options

• REPO_PATH: Path to Git repository
• VENV_PATH: Virtual environment path
• TEST_COMMAND: Command to run tests
• MAX_LINE_LENGTH: Maximum line length for code
• LINTER: Choice of linter (pylint/flake8/ruff)
• AIDER_MODEL: AI model selection
• MAX_CODE_LENGTH: Maximum code size
• MAX_MEMORY_MB: Memory limit
• API_RATE_LIMIT: API call limit

Resource Management

• Memory limit: Configurable via MAX_MEMORY_MB
• CPU usage monitoring
• Automatic temp file cleanup
• API rate limiting
• Database connection management
• Request size limits

Security Features

• Input validation and sanitization
• CSRF protection
• Content Security Policy
• Request size limits
• Secure cookie handling
• XSS protection
• Rate limiting
• Resource monitoring

Database

The application uses SQLite3 for storing:

• Code analysis suggestions
• AI responses
• File metadata
• Timestamps

Database file: suggestions.db Maintenance: Automatic cleanup based on configured thresholds

Web Interface

Start the web server:

python app.py

Access at http://localhost:8080

Available pages:

• /: Code analysis input
• /suggestions: Previous analyses
• /suggestion/<id>: Detailed view
• /analyze: New analysis

API Server

Start the API:

uvicorn suggestion_api:app --host 0.0.0.0 --port 8000

Command Line Tools

Aider Interrogator

python aider_interrogator.py --file /path/to/file.py --question "Your question"

Suggestion Management

python suggestion_cli.py [--id <id>] [--delete <id>] [--highlight]

Automated Agent

python agent_v2.py [--debug] [--max-workers <n>] [--file <path>]

Environment Variables

• DOCKER_ENV: Set to 1 for Docker
• FLASK_SECRET_KEY: Flask security key
• FLASK_ENV: development/production
• AIDER_API_KEY: Aider API key
• GOOGLE_CLOUD_PROJECT: GCP project ID

Error Handling

Common errors and solutions:

• Timeout: Reduce code size or increase timeout
• Rate limit: Wait and retry
• Resource limit: Increase limits or reduce load
• Validation: Check input requirements
• Database: Check permissions and space

Automatic retries implemented for:

• API calls
• Database operations
• Command execution

Logging

Logs include:

• Timestamp
• Log level
• File and line number
• Message

Log levels:

• INFO: Normal operations
• ERROR: Failures and exceptions
• DEBUG: Detailed debugging (when enabled)

Testing

Run tests:

python -m pytest

Test coverage includes:

• Unit tests
• Integration tests
• Resource management tests
• Security tests

Docker Deployment

docker build -t code-quality-tool .
docker run -p 8080:8080 -p 8000:8000 code-quality-tool

Development

This tool is under active development. Contributions welcome!

Best Practices

• Run tests before commits
• Check resource usage
• Monitor logs
• Review security settings
• Keep dependencies updated

Support

For issues and questions:

• Check logs
• Review configuration
• Verify dependencies
• Check resource usage
• Review documentation