Introduction

Why do some codebases feel easy to understand while others require constant mental translation? The difference often comes down to naming.

Naming communicates intent in code. Every variable, function, class, and module name tells a story about what it does and why it exists. Good names make code self-documenting, while poor names create confusion and slow development.

Most developers recognize that naming matters, but few understand the fundamentals. Without these principles, code accumulates unclear names that make changes risky and onboarding difficult. Understanding naming fundamentals helps teams write code that communicates clearly and is maintainable over time. Naming is a foundational element of software maintainability, enabling the readability and consistency that make systems maintainable over time.

What this is (and isn’t): This article explains naming principles and trade-offs, focusing on why good names matter and how naming decisions affect code quality. It doesn’t cover specific language naming conventions in detail, tool tutorials, or all possible naming patterns.

Why naming fundamentals matter:

  • Faster understanding - Good names reduce understanding time; some code reads like prose, others need constant lookup.

  • Fewer bugs - Clear names prevent misunderstandings, assumptions, and errors. They also lead to fewer bugs and quicker incident resolution.

  • Easier changes - Descriptive names clarify what needs changing and might break. Good naming makes code easier to modify.

  • Better collaboration - Consistent, clear names help developers understand each other’s code quickly, enabling smoother teamwork.

This article explains that effective naming relies on core principles: clarity, consistency, context, conventions, and discoverability, forming a system that makes code readable and maintainable.

This article outlines a basic workflow for naming:

  1. Choose clear names – names that communicate intent without requiring explanation
  2. Maintain consistency – follow established patterns so names are predictable
  3. Consider context – use appropriate specificity based on scope and visibility
  4. Follow conventions – align with team and language standards
  5. Enable discoverability – make code easy to find through search and navigation
The naming workflow: clarity enables understanding, consistency predictability, context appropriate specificity, conventions team alignment, discoverability finding code.

Type: Explanation (understanding-oriented).
Primary audience: beginner to intermediate software developers, team leads, and code reviewers learning why naming matters and how to improve it

Prerequisites & Audience

Prerequisites: Know basic programming concepts like variables, functions, and classes. Experience writing code helps, but no specific language knowledge is required.

Primary audience: Beginner to intermediate developers, team leads, and code reviewers seeking to understand why naming matters and how to improve it.

Jump to: Clarity • Consistency • Context • Conventions • Discoverability • Common Mistakes • Misconceptions • When NOT to Focus on Naming • Future Trends • Limitations & Specialists • Glossary

New developers should focus on clarity and consistency, while experienced developers can prioritize context and conventions.

Escape routes: For a quick refresher on clarity, read Section 1, then skip to “Common Naming Mistakes”.

TL;DR – Naming Fundamentals in One Pass

Understanding naming involves recognizing how core principles function as a system:

  • Clarity allows immediate understanding of code, reducing the need for comments. It answers “What does this do?”
  • Consistency enables predictability, helping developers infer meaning from patterns and answer “What pattern does this follow?”
  • Context enables appropriate specificity by using the right level of detail for the scope. It answers, “How specific should this name be?”
  • Conventions promote team alignment by setting shared standards and answering, “What does the team expect?”
  • Discoverability helps find code via search and navigation, making codebases easier to explore. It answers “How do I find this?”

These principles form a system: clarity enables understanding, supported by patterns; context guides specificity; conventions align teams; discoverability makes code findable. Each relies on the others for effectiveness.

The Naming Workflow:

The diagram illustrates five naming principles in a cycle: Clarity enables understanding, Consistency enables predictability, Context enables appropriate specificity, Conventions enable team alignment, and Discoverability enables finding code.

graph LR A[Clarity] --> B[Consistency] B --> C[Context] C --> D[Conventions] D --> E[Discoverability] E --> A style A fill:#e1f5fe style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#fff3e0 style E fill:#fce4ec

Figure 1. The naming system includes clarity for understanding, consistency for predictability, context for appropriateness, conventions for team alignment, and discoverability for finding code.

Learning Outcomes

By the end of this article, you will be able to:

  • Explain why clarity matters in naming and how clear names improve code readability.
  • Describe why consistency enables predictability and how patterns help developers understand code.
  • Explain why context determines appropriate specificity and when to use detailed versus concise names.
  • Explain how conventions facilitate team alignment and what constitutes effective naming standards.
  • Explain how discoverability aids developers in finding code and what naming practices enhance searchability.
  • Describe common naming patterns and when to use each approach.

Section 1: Clarity – Communicating Intent

Clarity means names instantly communicate intent, answering “what does this do?” or “what does this represent?” without explanation.

Think of clarity as a conversation. Reading code is like talking with the author. Clear names facilitate this, whereas unclear names create questions the code should answer.

Clarity Principles

Clarity comes from following specific principles:

Descriptive names use words that convey purpose. calculateTotalPrice() is clearer than calc(). Names should answer questions about purpose, not just state what they are.

Intent-revealing names communicate why something exists, not just what it is. isValidEmail() is clearer than check(). The name should reveal the purpose behind the code.

Use appropriate parts of speech: verbs for functions (calculateTotal(), validateInput()), nouns for variables (userAccount, orderTotal), adjectives for booleans (isValid, hasPermission), and question words for predicates (isValidEmail(), canEdit()).

Avoid abbreviations unless they’re universally understood. userAccount is clearer than usrAcct. Standard abbreviations like ID, URL, and API are acceptable. Use abbreviations consistently: if you use ID, don’t mix it with Id or identifier.

Avoid generic names that could mean anything. customerOrder is clearer than data. Generic names provide no information about purpose.

Why clarity works: Human cognition processes information better when it matches expected patterns. Clear names align with developers’ mental models, reducing cognitive load. When names communicate intent, developers can focus on logic rather than translation. This efficiency enables faster understanding and fewer misunderstandings.

Examples of Clarity

Here are examples showing clear versus unclear names:

Functions:

# Unclear
def proc(d):
    return d * 1.1

# Clear
def calculatePriceWithTax(price):
    return price * 1.1

Variables:

# Unclear
x = get_user_data()

# Clear
userProfile = get_user_data()

Booleans:

# Unclear
flag = check_status()

# Clear
isActive = check_status()

Classes:

# Unclear
class Mgr:
    pass

# Clear
class UserAccountManager:
    pass

Trade-offs and Limitations of Clarity

Clarity entails trade-offs: very long names hinder readability, and overly descriptive ones can be verbose. The goal is to balance, not maximize length.

When clarity isn’t enough: Clear names are essential but insufficient for maintainability. Good names can’t fix poor structure, missing abstractions, or unclear logic. Clarity helps, but other factors matter.

When Clarity Fails

Clarity suffers when names need mental translation or lookup, such as with abbreviations, generic terms, or mismatched names.

Signs of unclear naming: Developers frequently ask, “What does this do?” since names lack intent. Code reviews focus on explaining names instead of logic, while comments clarify intended meanings.

How unclear naming creates bugs: A developer thought process() handled orders, but it processed payments. Calling it with order data created a payment for the wrong amount. The unclear name caused the mistake, leading to incorrect usage and a bug. Using clear names like processPayment() could have prevented this.

Quick Check: Clarity

  • Can you understand a name’s purpose without reading the implementation?
  • Would a new team member understand the name immediately?
  • Does the name answer “what” and “why” questions?

If any answer is unclear, review names in your codebase: do they communicate intent immediately?

Section 2: Consistency – Predictable Patterns

Consistency ensures names follow predictable patterns in code, allowing developers to infer meaning from structure and reduce cognitive load.

Think of consistency as a language. Like grammar rules make sentences predictable, naming patterns make code predictable. Consistent patterns help developers understand code faster by recognizing structures.

Consistency Patterns

Consistency arises from following uniform patterns across a codebase:

Follow the same patterns for similar concepts. If you use getUserById(), also use getOrderById(), not fetchOrder(). Patterns enable inference.

Use consistent prefixes and suffixes for related concepts. is, has, can for booleans; get, set, create, delete for operations; Manager, Service, Handler for classes. Prefixes and suffixes communicate category and type.

Maintain naming style across the codebase. camelCase for variables and functions, PascalCase for classes, UPPER_CASE for constants. Style consistency reduces surprises.

Use consistent terminology for the same concepts. If you call it userAccount in one place, don’t call it account or user elsewhere. Terminology consistency prevents confusion.

Common naming patterns provide reusable structures:

  • Getter/setter patterns: getUser(), setUser() for retrieving or modifying values
  • Boolean patterns: isValid(), hasPermission(), canEdit() for boolean returns
  • Factory patterns: createUser(), makeOrder() for creating instances
  • Query patterns: findUser(), searchOrders(), getProduct() for retrieving data
  • Command patterns: sendEmail(), deleteUser(), updateOrder() for performing actions

Why consistency works: Human cognition quickly recognizes patterns. When code is consistent, developers infer meaning from structure, not details. This speeds understanding and reduces errors.

Examples of Consistency

Here are examples showing consistent versus inconsistent patterns:

Function naming:

# Inconsistent
def getUser():
    pass

def fetchOrder():
    pass

def retrieveProduct():
    pass

# Consistent
def getUser():
    pass

def getOrder():
    pass

def getProduct():
    pass

Boolean naming:

# Inconsistent
isValid = True
hasPermission = False
canEdit = True

# Consistent (using 'is' prefix)
isValid = True
isPermitted = False
isEditable = True

Class naming:

# Inconsistent
class UserMgr:
    pass

class OrderService:
    pass

class ProductHandler:
    pass

# Consistent (using 'Manager' suffix)
class UserManager:
    pass

class OrderManager:
    pass

class ProductManager:
    pass

Trade-offs and Limitations of Consistency

Consistency balances structure and flexibility. Strict patterns can seem rigid, and some cases benefit from exceptions. The aim is to be consistent where helpful and flexible when needed.

When consistency isn’t enough: Consistent patterns support clarity but don’t replace clear names or good structure. They amplify clarity but don’t create it.

When Consistency Fails

Consistency breaks when patterns are unpredictable or similar concepts have different names, causing confusion and slowing understanding.

Signs of inconsistency: Developers can’t predict names from patterns. Code reviews focus on style inconsistencies. New team members struggle to learn naming conventions.

Quick Check: Consistency

  • Do similar concepts follow the same naming patterns?
  • Can you predict names from patterns you’ve seen?
  • Does the codebase use consistent terminology?

If any answer is unclear, review naming patterns: do they follow consistent rules?

Section 3: Context – Appropriate Specificity

Context means names use appropriate specificity for their scope and visibility. A name in a small function can be shorter than a name in a global namespace, because context provides additional information.

Think of context as a conversation scope. In private, shorter references suffice as shared context fills in details. In public, more specificity is needed due to limited context.

Context Principles

Context determines appropriate specificity based on scope and visibility:

Local scope allows shorter names because surrounding code provides context. user in a function about users is clear, while user in a global namespace needs more specificity. i for a loop index is acceptable locally, but unclear as a global variable.

Module scope provides domain context; module names may use domain-specific terms to avoid ambiguity in the global namespace.

Private visibility allows shorter names; private methods can abbreviate, e.g., calcTotal() is fine as private but needs calculateTotal() if public.

Public visibility demands specific, self-documenting API names for use without context.

Domain context offers extra details that names can use. In banking, account is clear; in a generic system, bankAccount might be needed. Similarly, Order in e-commerce is clear, but in a general system, CustomerOrder may be necessary.

Why context works: Human cognition uses context to clarify meaning. When context supplies information, names can be shorter yet clear. Balancing specificity and brevity enhances readability.

Examples of Context

Examples illustrating suitable specificity for various contexts:

Local scope:

# Appropriate for local scope
def process_orders(orders):
    for order in orders:
        total = calculate_total(order)
        apply_discount(total)
    return orders

# Too specific for local scope
def process_orders(customer_order_list):
    for individual_customer_order in customer_order_list:
        order_total_amount = calculate_total_order_amount(individual_customer_order)
        apply_discount_to_order_total(order_total_amount)
    return customer_order_list

Module scope:

# Appropriate for module scope
# In user_account.py
class Account:
    pass

# Needs more specificity for global scope
# In main.py
class UserAccount:
    pass

Public vs private visibility:

# Appropriate: public method needs full specificity
class OrderProcessor:
    def calculate_total(self, order):
        pass

# Appropriate: private method can be shorter
class OrderProcessor:
    def _calc_tax(self, amount):
        pass

# Too brief for public API
class OrderProcessor:
    def calc(self, o):
        pass

Domain context:

# Appropriate in the e-commerce domain
class Order:
    def add_item(self, item):
        pass

# Needs more specificity in the generic system
class CustomerOrder:
    def add_order_item(self, order_item):
        pass

Trade-offs and Limitations of Context

Context requires balancing specificity with brevity. Over-relying on context hampers understanding; ignoring it leads to verbose names. The goal is appropriate specificity.

When context isn’t enough: Context supports clarity but doesn’t replace clear names or good structure. It amplifies clarity but doesn’t create it.

When Context Fails

Context fails when names depend on non-existent or changing context, causing confusion.

Signs of context problems: Names are unclear when the code is viewed in isolation. Code reviews focus on explaining context rather than logic. Refactoring breaks names that relied on context.

Quick Check: Context

  • Are names sufficiently specific for their scope?
  • Do public interfaces use self-documenting names?
  • Do local variables use context-appropriate brevity?

If any answer is unclear, review the names by scope: are they specific enough?

Section 4: Conventions – Team Alignment

Conventions are standards that teams agree on, ensuring consistent rules, predictable code, and fewer style debates.

Think of conventions as shared language, like grammar rules enable communication and naming conventions help team collaboration. When everyone follows the same, code reads consistently.

Common Conventions

Conventions originate from various sources, creating shared expectations:

Language conventions follow community standards. Python: snake_case for variables and functions, PascalCase for classes. JavaScript: camelCase for variables and functions, PascalCase for classes.

Framework conventions follow patterns established by frameworks. Rails: snake_case for files, PascalCase for classes. React: PascalCase for components, camelCase for props.

Domain-specific conventions follow domain patterns. REST APIs: resource names in URLs, HTTP methods for operations. Database: table names plural, column names singular.

Team conventions follow standards established by the development team. Teams may prefer getUser() over `fetchUser().

Industry conventions follow common patterns: isValid for booleans, Manager for coordinating classes.

Why conventions work: They create predictability and shared expectations. Following conventions allows developers to focus on logic rather than style, infer structure from names, and reduce decision fatigue, speeding understanding and facilitating quicker code reviews.

Examples of Conventions

Here are examples showing conventions in different contexts:

Python conventions:

# Follows Python conventions
class UserAccount:
    def get_user_by_id(self, user_id):
        user_name = "John"
        return user_name

# Violates Python conventions
class userAccount:
    def getUserById(self, userId):
        userName = "John"
        return userName

JavaScript conventions:

// Follows JavaScript conventions
class UserAccount {
    getUserById(userId) {
        const userName = "John";
        return userName;
    }
}

// Violates JavaScript conventions
class user_account {
    get_user_by_id(user_id) {
        const user_name = "John";
        return user_name;
    }
}

REST API conventions:

# Follows REST conventions
GET /api/users/123
POST /api/orders
PUT /api/products/456

# Violates REST conventions
GET /api/getUser?id=123
POST /api/createOrder
PUT /api/updateProduct?id=456

Trade-offs and Limitations of Conventions

Conventions involve trade-offs: strict adherence can feel limiting; in some situations, exceptions benefit. Follow conventions where helpful, be flexible where not.

When conventions aren’t enough: Conventions help, but can’t fix unclear names or poor structure; clarity is still essential.

When Conventions Fail

Conventions fail when unclear, inconsistently applied, or conflicting with other goals, creating confusion and reducing benefits.

Signs of convention problems: Developers debate conventions. Code reviews prioritize style over logic. New members struggle to learn them.

Quick Check: Conventions

  • Do names follow language and framework conventions?
  • Are team conventions documented and consistently applied?
  • Can new team members infer conventions from code?

If any answer is unclear, review conventions: are they clear and consistently followed?

Section 5: Discoverability – Finding Code

Discoverability ensures that names are easily findable via search, navigation, and exploration, enabling developers to locate what they need quickly.

Think of discoverability as a library catalog. Just as a good catalog helps you find books, good names help you find code. Discoverable names enable exploration and understanding of the codebase structure.

Discoverability Principles

Discoverability comes from following specific principles:

Searchable names use words that developers would search for. calculateTotalPrice() is more discoverable than calc(). Searchable names match how developers think about problems.

Distinctive names avoid generic terms that match too many results. processPayment() is more discoverable than process(). Distinctive names reduce search noise.

Hierarchical names reflect the organization of code. UserAccountManager indicates a manager in the user account domain. They enable navigation.

Consistent terminology uses the exact words throughout, making the search predictable. If you always use userAccount, a search for it returns all relevant code.

Why discoverability works: It enables exploration. When code is discoverable, developers find related code, understand the system structure, and learn the organization of the codebase. This speeds understanding and reduces duplication.

Examples of Discoverability

Here are examples showing discoverable versus non-discoverable names:

Searchable names:

# Discoverable
def calculate_total_price(items):
    pass

# Not discoverable
def calc(items):
    pass

Distinctive names:

# Discoverable
def process_payment(order):
    pass

# Not discoverable
def process(data):
    pass

Hierarchical names:

# Discoverable
class UserAccountManager:
    def create_user_account(self):
        pass

# Not discoverable
class Manager:
    def create(self):
        pass

Trade-offs and Limitations of Discoverability

Discoverability entails trade-offs: specific names can be verbose, whereas abbreviations may be more discoverable when commonly used. The aim is balance.

When discoverability isn’t enough: Discoverable names help find code, but can’t fix poor organization or missing abstractions. Discoverability aids in locating code, but good structure is also crucial.

When Discoverability Fails

Discoverability fails if names don’t match search habits or terminology is inconsistent, making code hard to find and understand.

Signs of discoverability problems: Developers struggle to find and explore code due to different terminology and search difficulties.

Quick Check: Discoverability

  • Can you find code by searching for domain terms?
  • Do names match how developers think about problems?
  • Is the terminology consistent enough for a comprehensive search?

If any answer is unclear, review names: are they discoverable through search?

Section 6: Common Naming Mistakes – What to Avoid

Common naming mistakes cause confusion and lower code quality. Recognizing these helps avoid them during code reviews.

Mistake 1: Generic Names

Generic names such as data, info, temp, and value provide no information about their purpose.

Incorrect:

def process(data):
    result = data * 2
    return result

Correct:

def calculate_double_price(price):
    doubled_price = price * 2
    return doubled_price

Mistake 2: Abbreviations That Aren’t Clear

Abbreviations that require lookup reduce clarity.

Incorrect:

usr_acct = UserAccount()
calc_tot = calculate_total()

Correct:

user_account = UserAccount()
total_price = calculate_total()

Mistake 3: Names That Lie

Names that don’t match the code’s actual behavior create confusion.

Incorrect:

def get_user():
    # Actually creates a new user
    return create_user()

Correct:

def create_user():
    return User()

Mistake 4: Inconsistent Patterns

Using different patterns for similar concepts reduces predictability.

Incorrect:

def getUser():
    pass

def fetchOrder():
    pass

def retrieveProduct():
    pass

Correct:

def getUser():
    pass

def getOrder():
    pass

def getProduct():
    pass

Mistake 5: Names That Don’t Scale

Names that work initially but become unclear as code grows.

Incorrect:

# Works initially, but becomes unclear as the system grows
class Manager:
    pass

class Service:
    pass

Correct:

# Scales as system grows
class UserAccountManager:
    pass

class OrderProcessingService:
    pass

Quick Check: Common Mistakes

  • Do names avoid generic terms that provide no information?
  • Are abbreviations clear and universally understood?
  • Do names accurately describe what the code does?
  • Are similar concepts named consistently?
  • Do names scale as the system grows?

If mistakes are found, refactor names for clarity, consistency, and context.

Section 7: Common Misconceptions

Common misconceptions about naming include:

  • “Shorter names are always better.” Brevity helps, but clarity matters more. calculateTotalPrice() is better than calc() even though it’s longer. Clarity enables understanding.

  • “Names don’t matter if code works.” Clear code is easier to maintain. Names impact long-term maintainability, not just functionality.

  • “Comments can explain unclear names.” Comments shouldn’t be needed to explain names. Clear names are self-documenting. Comments should explain why, not what.

  • “Naming conventions are arbitrary.” Conventions create predictability, enabling pattern recognition and reducing cognitive load.

  • “Good names take too long to write.” Good names save time by reducing confusion and enabling faster understanding. The time saved reading and understanding code far exceeds the time spent choosing names.

Section 8: When NOT to Focus on Naming

Naming isn’t always the top priority; knowing when to focus elsewhere helps allocate time better.

Prototyping and exploration - When exploring ideas, temporary names are acceptable. Focus on proving concepts first, then refactor names once validated.

One-off scripts - Scripts used once don’t need perfect names; focus on code that will be read and maintained.

Generated code - Generated code may have poor names, so refactoring might be pointless if code is often regenerated.

Legacy code with limited changes - Code that rarely changes may not justify a renaming effort. Focus on actively maintained code.

When performance is critical - In performance-critical code, some naming conventions may conflict with optimization needs. Balance clarity with performance requirements.

Even when you skip detailed naming, clarity is valuable. Use clear names for public interfaces and frequently-read code, even in prototypes or scripts.

Building Maintainable Codebases with Good Naming

Good naming is essential for maintainable code. Understanding naming principles helps teams write clear, long-lasting code.

Key Takeaways

  • Clarity enables understanding - Clear names instantly communicate intent, minimizing comments or documentation.

  • Consistency enables predictability - Consistent patterns help developers infer meaning quickly from structure.

  • Context determines specificity - Appropriate specificity based on scope and visibility balances clarity and brevity.

  • Conventions enable alignment - Following conventions ensures team alignment and minimizes style debates.

  • Discoverability enables exploration - Searchable, distinctive names simplify exploring and understanding codebases.

How These Principles Connect

Naming principles function as a system: Clarity provides the foundation; consistency enhances it via patterns; context guides specificity; conventions promote team alignment; discoverability enables exploration. Each principle supports the others.

These principles turn codebases needing constant mental translation into ones that read like prose. Proper naming helps developers spend less time translating and more understanding logic.

These principles support software maintainability. Clear, consistent names improve code quality, making systems easier to read, understand, and modify, reducing technical debt and enabling faster changes.

Getting Started with Better Naming

If you’re new to naming principles, start with a focused approach:

  1. Review existing code for unclear names that require explanation.
  2. Establish naming conventions for your team and document them.
  3. Refactor incrementally by improving names as you touch code.
  4. Use code reviews to catch naming issues before they accumulate.

Once this feels routine, expand the same principles to the rest of your codebase.

The Naming Workflow: A Quick Reminder

graph LR A[Clarity] --> B[Consistency] B --> C[Context] C --> D[Conventions] D --> E[Discoverability] style A fill:#e1f5fe style B fill:#f3e5f5 style C fill:#e8f5e8 style D fill:#fff3e0 style E fill:#fce4ec

Effective naming begins with clarity, consistent patterns, context-appropriate specificity, adherence to conventions, and discoverability for exploration.

Future Trends & Evolving Standards

Naming standards and practices evolve; understanding upcoming changes helps you prepare.

AI-Assisted Naming

AI tools are increasingly able to suggest and improve names based on context and code behavior.

What this means: Developers might depend more on AI for naming, but human judgment remains essential to ensure names clearly communicate intent.

How to prepare: Learn naming principles to evaluate AI suggestions effectively. Principles guide good decisions, even with AI help.

Domain-specific languages and frameworks are developing more specific naming conventions.

What this means: Naming may become more domain-specific with conventions tailored to specific problem areas.

How to prepare: Stay aware of domain conventions to communicate effectively within those areas domains.

Internationalization Considerations

As development teams expand globally, naming must account for multiple languages and cultures.

What this means: English may stay dominant, but teams should consider how names translate or function across languages.

How to prepare: Use clear, standard English terms that translate well. Avoid idioms or cultural references that don’t translate.

Limitations & When to Involve Specialists

Naming fundamentals offer a strong foundation, but certain situations need specialist expertise.

When Fundamentals Aren’t Enough

Some naming challenges extend beyond the fundamentals discussed in this article.

Large-scale refactoring: Renaming in large codebases needs tools and expertise to prevent breaking changes.

Domain-specific conventions: Some domains have strict naming requirements needing expertise.

Legacy system integration: Integrating with legacy systems may require understanding fixed naming conventions.

Glossary

Abbreviation: A shortened form of a word or phrase. Abbreviations can improve brevity but may reduce clarity if not universally understood.

Clarity: Names that quickly convey intent without explanation.

Consistency: The quality of names that follow predictable patterns throughout a codebase.

Context: The scope and visibility that determine name specificity.

Convention: A standard teams agree to follow for naming.

Discoverability: The quality of names that make code easy to find through search and navigation.

Identifier: A name that identifies a variable, function, class, or other code element.

Naming pattern: A reusable structure for common naming problems.

Scope: The context in which a name is visible and can be used.

Terminology: The words and phrases describing concepts in a domain or codebase.

Visibility: Access level to a name, like public, private, or protected.

References

Industry Standards

Tools & Resources

Community Resources

Note on Verification

Naming standards and best practices evolve. Verify current information and test naming decisions with actual code to ensure your names communicate clearly and enable maintainability.