Software principles form the foundation of systems that are clean, scalable, and easy to maintain. They serve as essential guidelines during design and development, helping create code that’s well-structured, readable, and adaptable. By following these principles, developers can reduce bugs and elevate the overall quality of their software.

DRY — Don’t Repeat Yourself

It’s a principle that encourages reducing repetition in code, especially logic, data, and structure. Instead of duplicating functionality, abstract it into reusable components.

🔍 Key Traits

  • Easier to maintain: fix it once, and it’s fixed everywhere.
  • Reduced cognitive load: less code duplication means fewer bugs.
  • Encourages abstraction: shared libraries, reusable components, common schemas.

✅ When It Helps

  • When similar logic appears in multiple places.
  • During refactoring or system growth—make reusable functions, services, or modules.
  • In database design, to avoid redundant data and maintain consistency.

⚠️ When It Hurts

  • Too early: Abstracting too soon can create complexity before patterns are stable.
  • Over-abstraction: Reusing code that doesn’t truly belong together can make maintenance harder.
  • Across domains: Don’t force DRY between unrelated features or contexts—it muddles clarity.

In distributed systems, blindly enforcing DRY across services (e.g., shared logic between microservices) can introduce unwanted coupling. Sometimes DRY inside a service, but WET between services is better.

WET — Write Everything Twice (or “We Enjoy Typing”)

Embrace duplication in certain contexts for clarity, autonomy, or speed. It refers to codebases with repetitive logic, structures, or data often resulting from copy-pasting or a lack of abstraction. While sometimes intentional, it’s usually an indicator of technical debt or missed opportunities for reuse. Think of it as the “opposite” of DRY — it leads to duplicated effort and tougher maintenance.

🔍 Key Traits

  • Duplication-friendly: code is repeated, not abstracted.
  • Fast to write: useful for quick fixes or prototypes.
  • Flexible logic: easily handles unique edge cases.
  • Readable alone: each block makes sense without shared dependencies.
  • Hard to maintain: changes in one place might require updates in many.

✅ When It Helps

  • Early prototyping: focus on velocity over elegance.
  • Isolation between bounded contexts: each domain owns its data, logic, and edge cases.
  • Microservices: duplication can reduce entanglement and increase autonomy.

Sometimes, repetition is the simpler, cleaner path—especially early on or in small scripts.

⚠️ When It Hurts

  • Creates inconsistency — same logic might behave differently in different places.
  • Changes require editing many files or functions, increasing bug risk.
  • Harder to test and harder to read over time.
  • Causes frustration when fixing widespread issues (“I’ve seen this bug in 5 places already…”)

KISS — Keep It Simple, Stupid

It emphasizes simplicity in design and implementation. The idea is that complexity increases the chances of bugs, confusion, and expensive maintenance — so keep things as simple as they need to be, no more. Simple code is easier to understand, debug, test, and modify. Use KISS to increase clarity and reduce mental overhead.

🔍 Key Traits

  • Simplicity first: prioritize clear, straightforward solutions over clever complexity.
  • Easy to maintain: simple code is easier to debug, test, and extend.
  • Faster development: less abstraction means quicker implementation and onboarding.
  • Readable and intuitive: code is easier to understand for new and experienced devs.
  • Avoid overengineering: don’t add what you don’t need — every extra layer invites risk.

✅ When It Helps

  • When designing interfaces, APIs, or architecture that others will use or maintain.
  • During refactoring — strip out convoluted logic and obscure workarounds.
  • When documenting or onboarding new developers.

⚠️ When It Hurts

  • Oversimplifying real problems: some problems require thoughtful complexity — KISS isn’t an excuse for ignoring nuance.
  • Avoiding necessary abstraction: sometimes multiple layers or components are needed.
  • In performance: critical paths, simplicity might reduce optimization.

KISS should make the code clearer, not strip away needed architecture or flexibility

YAGNI — You Aren’t Gonna Need It

Don’t build something until it’s needed. It reminds developers to avoid building features until they’re needed. Even if you’re sure you’ll need it later… chances are, you won’t or it’ll change anyway.

🔍 Key Traits

  • Saves time and effort: focus on solving today’s problems.
  • Avoids speculative features: cuts clutter and maintenance overhead.
  • Refactoring: makes refactoring easier because there’s less speculative code to maintain.

✅ When It Helps

  • MVPs and early-stage products: focuses effort on core value delivery.
  • Agile environments: adapts better to changing requirements.
  • Resource-constrained teams: saves time, money, and cognitive load.

⚠️ When It Hurts

  • Prototyping broad patterns: some upfront design is helpful for frameworks and extensibility.
  • Core infrastructure: skipping too much might cause painful rewrites later.
  • Low-cost abstractions: if the abstraction supports future variability and is cheap to implement, it might be worth bending a little.

Fail-Fast

The systems should detect and respond to errors as early as possible—ideally before cascading failures or side effects occur. This principle helps expose bugs quickly, simplify debugging, and prevent systems from operating in unstable or corrupt states.

🔍 Key Traits

  • Prevents cascading failures across services.
  • Conserves resources by terminating faulty operations early.
  • Boosts observability, failures show up quickly in logs and metrics.

✅ When It Helps

  • During input validation, config loading, or dependency setup.
  • In distributed systems, one faulty service can trigger a chain reaction.
  • For critical paths like authentication, payments, and telemetry ingestion.

⚠️ When It Hurts

  • In user-facing applications, abrupt failures without graceful messaging can be frustrating.
  • When failure conditions aren’t well-defined or lack context.
  • If excessive “fail-fast” logic causes false positives or overly strict rejection.

Premature Optimization

Premature optimization means trying to improve performance too early in the development process, often before functionality is complete or bottlenecks are understood. It’s like tuning the engine before knowing if the car drives straight.

🔍 Key Traits

  • Optimization before validation: tTweaking performance without a working baseline.
  • Assumption-driven: decisions made without profiling or real data.
  • Complexity creep: adds unnecessary abstraction or low-level tweaks.
  • Readability trade-offs: sacrifices clarity for marginal gains.
  • Misplaced priorities: focuses on speed over correctness or maintainability.

✅ When It Helps

  • The system is stable and feature-complete.
  • You’ve gathered real metrics and identified slow paths.
  • You know which parts of the code matter most for performance.

Use profiling tools first, then optimize with purpose.

⚠️ When It Hurts

  • During prototyping or early development, where requirements are evolving.
  • Before validating correctness, you risk optimizing broken or throwaway code.
  • When done blindly, leading to unreadable logic, poor modularity, or wasted effort on non-critical paths.

SoC — Separation of Concerns

Split a system into parts that each handle a distinct “concern” like UI, business logic, data access, or messaging. A “concern” could be anything from rendering HTML to storing metrics or publishing Kafka events.

🔍 Key Traits

  • Modular thinking: break systems into distinct parts, each handling a specific responsibility.
  • Independent evolution: changes in one concern don’t ripple through unrelated areas.
  • Improved testability: isolated components are easier to test and debug.
  • Reusability boost: well-separated logic can be reused across projects or layers.
  • Simplified maintenance: easier to locate, fix, or extend functionality without side effects

⚠️ When It Hurts

  • Over-separation: excessive fragmentation can lead to complexity and cognitive overload.
  • Tight coupling via interfaces: poorly designed boundaries may still create hidden dependencies.
  • Loss of cohesion: splitting logic that naturally belongs together can reduce clarity.
  • Integration overhead: more components mean more glue code and orchestration.
  • Premature abstraction: separating concerns before understanding the domain can lead to brittle designs.

Modularity

Modularity is a design principle that breaks a system into independent, self-contained components (modules), each responsible for a specific functionality. These modules interact through well-defined interfaces, making the system easier to build, understand, and evolve. Think of it like building with LEGO blocks—each piece has a clear shape and purpose, but together they form something much larger.

🔍 Key Traits

  • Complexity management: simplifies large systems by dividing them into manageable parts.
  • Independent development: teams can work on different modules without stepping on each other’s toes.
  • Isolated testing: modules can be tested individually, improving reliability.
  • Reusability: modules can be reused across projects or within different parts of the same system.
  • Scalability: easier to extend or replace parts without affecting the whole system.

⚠️ When It Hurts

  • Over-modularization: too many tiny modules can lead to fragmentation and cognitive overload.
  • Interface complexity: poorly designed interfaces can create tight coupling or hidden dependencies.
  • Integration overhead: more modules mean more coordination and glue code.
  • Performance trade-offs: excessive abstraction may introduce latency or resource overhead.

LoD — The Law of Demeter

Also called the “principle of least knowledge”. LoD is a design guideline that encourages objects to only interact with their immediate collaborators, not with the internal details of other objects.

🔍 Key Traits

  • Loose coupling: changes in one class don’t ripple through others.
  • Better testability: easier to mock and isolate components.
  • Improved readability: code is easier to follow when interactions are localized.
  • Safer refactoring: fewer dependencies mean fewer surprises during changes.
  • Encapsulation enforcement: keeps internal structures hidden and protected.

⚠️ When It Hurts

  • Over-abstraction: can lead to excessive wrapper methods or indirection.
  • Performance overhead: extra method calls may introduce latency.
  • Interface bloat: classes may expose too many forwarding methods just to comply.
  • Reduced transparency: hiding structure can obscure useful relationships

PoLA — Principle of Least Astonishment

PoLA suggests that software should behave in a way that’s intuitive and predictable to users and developers. If something works differently than expected, it creates confusion, bugs, and frustration.

🔍 Key Traits

  • Improves usability: users feel comfortable and confident navigating the system.
  • Reduces bugs: predictable behavior means fewer misunderstandings and edge cases.
  • Enhances readability: developers can guess what the code does without deep dives.
  • Simplifies onboarding: new team members ramp up faster with familiar patterns.
  • Strengthens API design: clear naming and behavior reduce misuse.

⚠️ When It Hurts

  • Over-simplification: trying too hard to match expectations may hide necessary complexity.
  • Conflicting expectations: different users may expect different behaviors.
  • Rigid adherence: avoiding innovation just to stay familiar can stifle progress.
  • Legacy baggage: sticking to outdated conventions may confuse modern users

Make It Run, Make It Right, Make It Fast

Make It Run

Just get it working.

🔍 Key Traits

  • Quick functionality, rapid prototyping, minimal structure.

✅ When to Use

  • Early-stage exploration, validating concepts, debugging unknowns.

⚠️ When It Hurts

  • Scaling on unstable code, bypassing standards leads to brittle systems.

Make It Right

Refactor toward correctness and clarity.

🔍 Key Traits

  • Clean design, stable abstractions, test coverage, SOLID alignment.

✅ When to Use

  • Post-prototype refinement, preparing for handoff or scale, enabling longevity.

⚠️ When It Hurts

  • Overengineering too early, delaying valuable feedback cycles.

Make It Fast

Optimize performance when you know it’s worth it.

🔍 Key Traits

  • Performance tuning, profiling-driven decisions, optimized resources.

✅ When to Use

  • Bottleneck resolution, high-load environments, latency-sensitive systems.

⚠️ When It Hurts

  • Premature optimization before correctness, hard-to-maintain micro-tweaks.

SOLID

SOLID is an acronym coined by Robert C. Martin (Uncle Bob) that represents five key principles of object-oriented design:

  • S: Single Responsibility Principle (SRP)
  • O: Open/Closed Principle (OCP)
  • L: Liskov Substitution Principle (LSP)
  • I: Interface Segregation Principle (ISP)
  • D: Dependency Inversion Principle (DIP)

These principles help reduce coupling, improve cohesion, and make systems easier to extend and refactor.

Single Responsibility Principle (SRP)

A class should have only one reason to change.

  • Encourages modularity and separation of concerns.
  • Makes testing and debugging easier.
  • Violations often lead to bloated classes doing too much.

Example: A ReportGenerator class should not also handle file I/O. Instead, split responsibilities into ReportGenerator and FileWriter.

Open/Closed Principle (OCP)

Software entities should be open for extension, but closed for modification.

  • You can add new behavior without altering existing code.
  • Promotes polymorphism and abstraction.
  • Violations often occur with switch statements or if-else chains.

Example: Instead of modifying a PaymentProcessor to support PayPal, create a new PayPalProcessor that implements a common interface.

Liskov Substitution Principle (LSP)

Subtypes must be substitutable for their base types without altering correctness.

  • Ensures consistent behavior across inheritance hierarchies.
  • Prevents unexpected side effects when using derived classes.
  • Violations often occur when overriding methods break expectations.

Example: If Bird has a fly() method, a Penguin subclass that can’t fly violates LSP unless behavior is restructured.

Interface Segregation Principle (ISP)

Clients should not be forced to depend on interfaces they don’t use.

  • Encourages small, focused interfaces.
  • Improves clarity and reduces unnecessary implementation.
  • Violations often lead to “fat” interfaces with unused methods.

Example: Instead of one IMachine interface with Print(), Scan(), and Fax(), split into IPrinter, IScanner, and IFax.

Dependency Inversion Principle (DIP)

High-level modules should not depend on low-level modules. Both should depend on abstractions.

  • Promotes loose coupling and testability.
  • Encourages use of interfaces and dependency injection.
  • Violations often occur when classes instantiate their dependencies.

Example: A NotificationService should depend on an INotificationSender interface, not directly on EmailSender.