coding-principles

name	coding-principles
description	Language-agnostic coding principles for maintainability, readability, and quality. Use when implementing features, refactoring code, or reviewing code quality.

Language-Agnostic Coding Principles

Core Philosophy

1. Maintainability over Speed: Prioritize long-term code health over initial development velocity
2. Simplicity First: Choose the simplest solution that meets requirements (YAGNI principle)
3. Explicit over Implicit: Make intentions clear through code structure and naming
4. Delete over Comment: Remove unused code instead of commenting it out

Code Quality

Continuous Improvement

• Refactor as you go - don't accumulate technical debt
• Improve code structure incrementally
• Keep the codebase lean and focused
• Delete unused code immediately

Readability

• Use meaningful, descriptive names drawn from the problem domain
• Avoid abbreviations unless they are widely recognized
• Avoid single-letter names except for loop counters or well-known conventions (i, j, x, y)
• Extract magic numbers and strings into named constants
• Keep code self-documenting where possible
• Write code that humans can easily understand

Function Design

Parameter Management

• Recommended: 0-2 parameters per function
• For 3+ parameters: Use objects, structs, or dictionaries to group related parameters
• Example (conceptual):

  // Instead of: createUser(name, email, age, city, country)
  // Use: createUser(userData)
  

Single Responsibility

• Each function should do one thing well
• Keep functions small and focused (typically < 50 lines)
• Extract complex logic into separate, well-named functions
• Functions should have a single level of abstraction

Function Organization

• Pure functions when possible (no side effects)
• Separate data transformation from side effects
• Use early returns to reduce nesting
• Avoid deep nesting (maximum 3 levels)

Error Handling

Error Management Principles

• Always handle errors: Log with context or propagate explicitly
• Log appropriately: Include context for debugging
• Protect sensitive data: Mask or exclude passwords, tokens, PII from logs
• Fail fast: Detect and report errors as early as possible

Error Propagation

• Use language-appropriate error handling mechanisms
• Propagate errors to appropriate handling levels
• Provide meaningful error messages
• Include error context when re-throwing

Dependency Management

Loose Coupling via Parameterized Dependencies

• Inject external dependencies as parameters (constructor injection for classes, function parameters for procedural/functional code)
• Depend on abstractions, not concrete implementations
• Minimize inter-module dependencies
• Facilitate testing through mockable dependencies

Performance Considerations

Optimization Approach

• Measure first: Profile before optimizing
• Focus on algorithms: Algorithmic complexity > micro-optimizations
• Use appropriate data structures: Choose based on access patterns
• Resource management: Handle memory, connections, and files properly

When to Optimize

• After identifying actual bottlenecks
• When performance issues are measurable
• Not prematurely during initial development

Code Organization

Structural Principles

• Group related functionality: Keep related code together
• Separate concerns: Domain logic, data access, presentation
• Consistent naming: Follow project conventions
• Module cohesion: High cohesion within modules, low coupling between

File Organization

• One primary responsibility per file
• Logical grouping of related functions/classes
• Clear folder structure reflecting architecture
• Avoid "god files" (files > 500 lines)

Commenting Principles

When to Comment

• Document "what": Describe what the code does
• Explain "why": Clarify reasoning behind decisions
• Note limitations: Document known constraints or edge cases
• API documentation: Public interfaces need clear documentation

When NOT to Comment

• Avoid describing "how" (the code shows that)
• Don't include historical information (use version control)
• Remove commented-out code (use git to retrieve old code)
• Avoid obvious comments that restate the code

Comment Quality

• Keep comments concise and timeless
• Update comments when changing code
• Use proper grammar and formatting
• Write for future maintainers

Refactoring Approach

Safe Refactoring

• Small steps: Make one change at a time
• Maintain working state: Keep tests passing
• Verify behavior: Run tests after each change
• Incremental improvement: Don't aim for perfection immediately

Refactoring Triggers

• Code duplication (DRY principle)
• Functions > 50 lines
• Complex conditional logic
• Unclear naming or structure

Testing Considerations

Testability

• Write testable code from the start
• Avoid hidden dependencies
• Keep side effects explicit
• Design for parameterized dependencies

Test-Driven Development

• Write tests before implementation when appropriate
• Keep tests simple and focused
• Test behavior, not implementation
• Maintain test quality equal to production code

Security Principles

General Security

• Store secrets in environment variables or secret managers
• Validate all external input
• Use parameterized queries for databases
• Follow principle of least privilege

Data Protection

• Encrypt sensitive data at rest and in transit
• Sanitize user input
• Avoid logging sensitive information
• Use secure random generators for security-critical operations

Documentation

Code Documentation

• Document public APIs and interfaces
• Include usage examples for complex functionality
• Maintain README files for modules
• Keep documentation in sync with code

Architecture Documentation

• Document high-level design decisions
• Explain integration points
• Clarify data flows and boundaries
• Record trade-offs and alternatives considered

Version Control Practices

Commit Practices

• Make atomic, focused commits
• Write clear, descriptive commit messages
• Commit working code (passes tests)
• Avoid committing debug code or secrets

Code Review Readiness

• Self-review before requesting review
• Keep changes focused and reviewable
• Provide context in pull request descriptions
• Respond to feedback constructively

Language-Specific Adaptations

While these principles are language-agnostic, adapt them to your specific programming language:

• Static typing: Use strong types when available
• Dynamic typing: Add runtime validation
• OOP languages: Apply SOLID principles
• Functional languages: Prefer pure functions and immutability
• Concurrency: Follow language-specific patterns for thread safety