Skip to content

CODE_QUALITY.md

Latest commit

 

History

History
325 lines (239 loc) · 9.4 KB

CODE_QUALITY.md

File metadata and controls

325 lines (239 loc) · 9.4 KB

Code Quality Guidelines

Overview

MeridianAlgo maintains high standards for code quality to ensure our projects are maintainable, secure, and performant. These guidelines apply to all contributions across our repositories.

General Principles

Code Quality Standards

  • Readability First: Code should be self-documenting and easy to understand
  • Consistency: Follow established patterns and conventions within each project
  • Testability: Write code that can be easily tested and validated
  • Performance: Consider performance implications without premature optimization
  • Security: Follow secure coding practices at all times

Documentation Requirements

  • Public APIs: Must have comprehensive documentation
  • Complex Logic: Include inline comments explaining the approach
  • Configuration: Document all configuration options and their defaults
  • Dependencies: Document required dependencies and their versions

Language-Specific Guidelines

Python

Style Guide

  • Follow PEP 8 style guidelines
  • Use Black for code formatting
  • Maximum line length: 88 characters
  • Use isort for import organization

Code Structure

# Imports (standard library, third-party, local)
import os
import sys
from typing import List, Optional

import numpy as np
import pandas as pd

from meridianalgo.core import BaseClass

# Constants
DEFAULT_TIMEOUT = 30
MAX_RETRIES = 3

# Classes and functions
class DataProcessor:
    """Process financial data with validation and error handling."""
    
    def __init__(self, config: dict) -> None:
        self.config = config
        self._validate_config()
    
    def process(self, data: List[dict]) -> Optional[pd.DataFrame]:
        """Process input data and return validated DataFrame.
        
        Args:
            data: List of data dictionaries to process
            
        Returns:
            Processed DataFrame or None if processing fails
            
        Raises:
            ValueError: If data format is invalid
        """
        pass

Testing

  • Use pytest for unit testing
  • Maintain 80%+ test coverage
  • Write both unit and integration tests
  • Use type hints for better IDE support

JavaScript/TypeScript

Style Guide

  • Use ESLint with recommended configurations
  • Use Prettier for code formatting
  • Prefer TypeScript for new projects
  • Use CamelCase for variables, PascalCase for classes

Code Structure

// Imports
import { Logger } from './utils/logger';
import { DataProcessor } from './core/processor';

// Constants
const DEFAULT_TIMEOUT = 30000;
const MAX_RETRIES = 3;

// Interfaces
interface ProcessedData {
  id: string;
  timestamp: number;
  value: number;
}

// Classes
export class FinancialProcessor {
  private logger: Logger;
  
  constructor(config: ProcessorConfig) {
    this.logger = new Logger('FinancialProcessor');
    this.validateConfig(config);
  }
  
  /**
   * Process financial data with validation
   * @param data - Raw data to process
   * @returns Processed data or null if validation fails
   */
  public processData(data: RawData[]): ProcessedData[] | null {
    try {
      return this.validateAndTransform(data);
    } catch (error) {
      this.logger.error('Processing failed', error);
      return null;
    }
  }
}

Testing

  • Use Jest for unit testing
  • Maintain 80%+ test coverage
  • Include both unit and integration tests
  • Use TypeScript for type safety

Code Review Process

Pre-Commit Checklist

Before submitting a pull request, ensure:

  • Code follows project style guidelines
  • All tests pass locally
  • New code has appropriate test coverage
  • Documentation is updated
  • No sensitive data is committed
  • Dependencies are properly declared
  • Error handling is implemented
  • Code is self-contained and reproducible

Review Criteria

Pull requests are evaluated based on:

  1. Functionality: Does the code work as intended?
  2. Quality: Is the code well-written and maintainable?
  3. Testing: Are tests comprehensive and reliable?
  4. Documentation: Is the code properly documented?
  5. Security: Does the code follow security best practices?
  6. Performance: Are there any performance concerns?

Testing Guidelines

Unit Testing

  • Test Coverage: Minimum 80% line coverage
  • Test Organization: Group related tests together
  • Test Naming: Use descriptive names that explain the scenario
  • Mocking: Mock external dependencies appropriately

Integration Testing

  • End-to-End Scenarios: Test complete workflows
  • Database Integration: Test database interactions
  • API Testing: Test API endpoints and responses
  • Performance Testing: Include performance benchmarks where relevant

Test Structure Example

import pytest
from unittest.mock import Mock, patch
from meridianalgo.processor import DataProcessor

class TestDataProcessor:
    
    def setup_method(self):
        """Setup test fixtures before each test."""
        self.config = {"timeout": 30, "retries": 3}
        self.processor = DataProcessor(self.config)
    
    def test_process_valid_data_success(self):
        """Test processing of valid data returns expected results."""
        # Arrange
        test_data = [{"id": 1, "value": 100}]
        
        # Act
        result = self.processor.process(test_data)
        
        # Assert
        assert result is not None
        assert len(result) == 1
        assert result[0]["id"] == 1
    
    def test_process_invalid_data_raises_error(self):
        """Test processing invalid data raises ValueError."""
        # Arrange
        invalid_data = [{"invalid": "data"}]
        
        # Act & Assert
        with pytest.raises(ValueError, match="Invalid data format"):
            self.processor.process(invalid_data)
    
    @patch('meridianalgo.processor.external_api')
    def test_process_with_external_dependency(self, mock_api):
        """Test processing with mocked external dependency."""
        # Arrange
        mock_api.return_value = {"status": "success"}
        
        # Act
        result = self.processor.process_with_api()
        
        # Assert
        assert result["status"] == "success"
        mock_api.assert_called_once()

Performance Guidelines

Code Optimization

  • Profiling: Profile code before optimizing
  • Bottlenecks: Focus on critical performance bottlenecks
  • Memory Management: Consider memory usage and cleanup
  • Caching: Implement appropriate caching strategies

Database Operations

  • Query Optimization: Use efficient queries and indexes
  • Connection Management: Properly manage database connections
  • Batch Operations: Use batch processing for large datasets
  • Transaction Management: Handle transactions appropriately

Security Guidelines

Input Validation

  • Sanitize Inputs: Validate and sanitize all user inputs
  • Type Checking: Use strict type checking
  • Length Limits: Implement appropriate length limits
  • Format Validation: Validate data formats and patterns

Authentication & Authorization

  • Secure Storage: Never store credentials in code
  • Environment Variables: Use environment variables for secrets
  • Least Privilege: Apply principle of least privilege
  • Session Management: Implement secure session handling

Tools and Automation

Required Tools

  • Linting: ESLint, Pylint, or equivalent
  • Formatting: Prettier, Black, or equivalent
  • Testing: Jest, pytest, or equivalent
  • Type Checking: TypeScript, mypy, or equivalent

CI/CD Integration

  • Automated Testing: Run tests on every commit
  • Code Quality: Automated code quality checks
  • Security Scanning: Automated vulnerability scanning
  • Dependency Checks: Automated dependency vulnerability checks

Documentation Standards

Code Documentation

  • Docstrings: Comprehensive docstrings for all public functions
  • Comments: Explain complex logic and business rules
  • Examples: Provide usage examples in documentation
  • Changelog: Maintain changelog for significant changes

API Documentation

  • OpenAPI/Swagger: For REST APIs
  • JSDoc: For JavaScript APIs
  • Sphinx: For Python documentation
  • Interactive Examples: Include working examples

Monitoring and Logging

Logging Standards

  • Structured Logging: Use structured logging formats
  • Log Levels: Appropriate use of log levels (DEBUG, INFO, WARN, ERROR)
  • Sensitive Data: Never log sensitive information
  • Performance Metrics: Include performance metrics in logs

Error Handling

  • Graceful Degradation: Handle errors gracefully
  • Error Messages: Provide clear, actionable error messages
  • Recovery: Implement error recovery mechanisms
  • Monitoring: Monitor error rates and patterns

Continuous Improvement

Code Quality Metrics

  • Code Coverage: Track and maintain test coverage
  • Code Complexity: Monitor cyclomatic complexity
  • Technical Debt: Track and address technical debt
  • Performance Metrics: Monitor key performance indicators

Regular Reviews

  • Monthly Audits: Regular code quality audits
  • Tool Updates: Keep tools and dependencies updated
  • Guideline Updates: Review and update guidelines regularly
  • Training: Provide ongoing training for contributors

These guidelines are regularly updated to reflect best practices and community feedback. Last updated: January 2026