General Code Style Guide

Universal coding principles that apply across all languages and frameworks.

Readability

Code is Read More Than Written

• Write code for humans first, computers second
• Favor clarity over cleverness
• If code needs a comment to explain what it does, consider rewriting it

Formatting

• Consistent indentation (use project standard)
• Reasonable line length (80-120 characters)
• Logical grouping with whitespace
• One statement per line

Structure

• Keep functions/methods short (ideally < 20 lines)
• One level of abstraction per function
• Early returns to reduce nesting
• Group related code together

Naming Conventions

General Principles

• Names should reveal intent
• Avoid abbreviations (except universally understood ones)
• Be consistent within codebase
• Length proportional to scope

Variables

# Bad
d = 86400  # What is this?
temp = getUserData()  # Temp what?

# Good
secondsPerDay = 86400
userData = getUserData()

Functions/Methods

• Use verbs for actions: calculateTotal(), validateInput()
• Use is/has/can for booleans: isValid(), hasPermission()
• Be specific: sendEmailNotification() not send()

Constants

• Use SCREAMING_SNAKE_CASE or language convention
• Group related constants
• Document magic numbers

Classes/Types

• Use nouns: User, OrderProcessor, ValidationResult
• Avoid generic names: Manager, Handler, Data

Comments

When to Comment

• WHY, not WHAT (code shows what, comments explain why)
• Complex algorithms or business logic
• Non-obvious workarounds with references
• Public API documentation

When NOT to Comment

• Obvious code
• Commented-out code (delete it)
• Change history (use git)
• TODOs without tickets (create tickets instead)

Comment Quality

# Bad
i += 1  # Increment i

# Good
# Retry limit based on SLA requirements (see JIRA-1234)
maxRetries = 3

Error Handling

Principles

• Fail fast and explicitly
• Handle errors at appropriate level
• Preserve error context
• Log for debugging, throw for callers

Patterns

# Bad: Silent failure
try:
    result = riskyOperation()
except:
    pass

# Good: Explicit handling
try:
    result = riskyOperation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")
    raise OperationFailed("Unable to complete operation") from e

Error Messages

• Be specific about what failed
• Include relevant context
• Suggest remediation when possible
• Avoid exposing internal details to users

Functions and Methods

Single Responsibility

• One function = one task
• If you need "and" to describe it, split it
• Extract helper functions for clarity

Parameters

• Limit parameters (ideally ≤ 3)
• Use objects/structs for many parameters
• Avoid boolean parameters (use named options)
• Order: required first, optional last

Return Values

• Return early for edge cases
• Consistent return types
• Avoid returning null/nil when possible
• Consider Result/Option types for failures

Code Organization

File Structure

• One primary concept per file
• Related helpers in same file or nearby
• Consistent file naming
• Logical directory structure

Import/Dependency Order

1. Standard library
2. External dependencies
3. Internal dependencies
4. Local/relative imports

Coupling and Cohesion

• High cohesion within modules
• Low coupling between modules
• Depend on abstractions, not implementations
• Avoid circular dependencies

Testing Considerations

Testable Code

• Pure functions where possible
• Dependency injection
• Avoid global state
• Small, focused functions

Test Naming

# Describe behavior, not implementation
test_user_can_login_with_valid_credentials()
test_order_total_includes_tax_and_shipping()

Security Basics

Input Validation

• Validate all external input
• Sanitize before use
• Whitelist over blacklist
• Fail closed (deny by default)

Secrets

• Never hardcode secrets
• Use environment variables or secret managers
• Don't log sensitive data
• Rotate credentials regularly

Data Handling

• Minimize data collection
• Encrypt sensitive data
• Secure data in transit and at rest
• Follow principle of least privilege

Performance Mindset

Premature Optimization

• Make it work, then make it fast
• Measure before optimizing
• Optimize bottlenecks, not everything
• Document performance-critical code

Common Pitfalls

• N+1 queries
• Unnecessary allocations in loops
• Missing indexes
• Synchronous operations that could be async

Code Review Checklist

• Does it work correctly?
• Is it readable and maintainable?
• Are edge cases handled?
• Is error handling appropriate?
• Are there security concerns?
• Is it tested adequately?
• Does it follow project conventions?
• Is there unnecessary complexity?