In software development, ensuring that a system is scalable and maintainable is crucial. This article explores insights from software engineering materials, particularly from The Hong Kong University of Science and Technology, focusing on Design Patterns, which offer proven solutions to recurring software design challenges.
Design patterns are structured solutions that address common coding problems. They are categorized as follows:
Creational Patterns: Object Creation Strategies for Flexibility and Efficiency
- Singleton → Ensures only one instance of a class exists, useful for managing global resources like database connections.
- Factory → Centralizes object creation to avoid exposing instantiation logic, improving flexibility.
- Builder → Constructs complex objects step by step, making code more readable and maintainable
- Prototype → Clones existing objects instead of creating new ones, reducing initialization cost.
- Abstract Factory → Creates families of related objects without specifying concrete classes, ensuring consistency.
Structural Patterns: Organizing Code for Modularity and Maintainability
- Adapter → Bridges incompatible interfaces so they can work together.
- Bridge → Separates abstraction from implementation, making systems more flexible.
- Composite → Treats groups of objects like individual ones, useful for hierarchical structures.
- Decorator → Dynamically adds functionality to objects without modifying their structure.
- Façade → Provides a simple interface to a complex system, improving usability.
- Flyweight → Reduces memory usage by sharing common object data.
- Proxy → Controls access to an object, adding features like caching and lazy loading.
Behavioral Patterns: Manage Object Interactions and Responsibility Distribution
- Observer → Allows multiple objects to react to state changes in another object.
- Strategy → Enables selecting an algorithm at runtime without modifying client code.
- Command → Encapsulates requests as objects, allowing undo/redo functionality.
- Mediator → Reduces dependencies between objects by introducing a central coordinator.
- Chain of Responsibility → Passes a request through handlers until one processes it.
- State → Changes object behavior dynamically based on its internal state.
- Visitor → Separates algorithms from objects, making it easy to add new operations.
- Iterator → Provides a way to traverse a collection without exposing its internal details.
Concurrency Patterns: Handle Multi-Threaded Operations Efficiently
- Thread Pool → Reuses a pool of threads to handle multiple tasks efficiently.
- Monitor Object → Ensures only one thread accesses a critical section at a time.
- Reactor → Handles multiple asynchronous events using a single event loop.
- Read-Write Lock → Allows multiple reads but restricts writes to one thread at a time.
Throughout my studies at The Hong Kong University of Science and Technology, I discovered how design patterns improve software quality and prevent common pitfalls. Many developers overlook them at first, but their benefits include:
✅ Improved Code Readability → Developers can understand code intent quickly.
✅ Faster Development → Reusing established patterns saves development time.
✅ Better Scalability → Structured code is easier to expand without breaking existing functionality.
✅ Avoiding Anti-Patterns → Prevents poorly structured, difficult-to-maintain code.
One example discussed in software engineering is The Mediator Pattern, useful in systems where multiple components need to communicate while minimizing direct dependencies.
Advantages of the Mediator Pattern
✅ Decoupled Colleagues → Objects no longer communicate directly, making them independent.
✅ Improved Readability & Maintenance → Centralized logic makes it easier to understand and modify.
✅ Simplified Communication → Reduces complexity by converting many-to-many relationships into one-to-many.
✅ Less Subclassing → Changes in communication logic only affect the mediator, not individual components.the mediator class, rather than altering multiple interconnected classes.
Disadvantages of the Mediator Pattern
⚠️ Increased Complexity → The mediator itself can become overly complex if it handles too many interactions.
interface Mediator {
fun notify(sender: String, event: String)
}class SmartHomeMediator : Mediator {
private val devices = mutableMapOf()
fun registerDevice(name: String, device: SmartDevice) {
devices[name] = device
}
override fun notify(sender: String, event: String) {
when (event) {
"AlarmTriggered" -> devices["Heater"]?.action("TurnOn")
"Morning" -> devices["CoffeeMachine"]?.action("Start")
}
}
}
By using a Mediator, devices communicate indirectly, making the system easier to manage and extend.
The Proxy Pattern is used when direct access to an object needs to be controlled or optimized. Instead of interacting with the actual object immediately, a proxy acts as a middle layer that decides when and how the real object should be created or accessed.
Example Use Case: Image Viewer
An image viewer application should only load high-resolution images when necessary, rather than loading all at once.
interface Image {
fun display()
}// The real object that takes time to initialize
class RealImage(private val filename: String) : Image {
init {
println("Loading image: $filename") // Simulating a costly operation
}
override fun display() {
println("Displaying image: $filename")
}
}
// Proxy that defers the creation of RealImage
class ImageProxy(private val filename: String) : Image {
private var realImage: RealImage? = null
override fun display() {
if (realImage == null) {
realImage = RealImage(filename) // Object is created only when needed
}
realImage?.display()
}
}
// Usage
fun main() {
val image1: Image = ImageProxy("photoku.jpg")
val image2: Image = ImageProxy("photomu.png")
println("Accessing images...")
image1.display() // Loads and displays image
image2.display() // Loads and displays image
}
Benefits of the Proxy Pattern
✅ Optimized Performance → The real object is only created when necessary, reducing memory and processing overhead.
✅ Access Control → Proxy can act as a security layer, restricting unauthorized access.
✅ Lazy Loading → Objects are loaded on demand, preventing unnecessary instantiation.
Potential Downsides
⚠️ Increased Complexity → Adds an extra layer, which may complicate debugging.
⚠️ Delay in Object Access → Since the real object is created when first needed, there might be a slight performance hit during the first request.
The Proxy Pattern is widely used in virtual proxies (lazy loading), protection proxies (access control), and remote proxies (network communication).
The Singleton Pattern ensures that only one instance of a class exists throughout an application. This is useful for shared resources that should not be duplicated, such as database connections, logging systems, or configuration settings.
A database connection should be shared rather than created multiple times. Opening multiple connections unnecessarily can slow down the system and waste resources. With Singleton, the connection is created once and reused whenever needed.
Example Use Case: Database Connection Manager
A database connection should be shared rather than created multiple times. Opening multiple connections unnecessarily can slow down the system and waste resources. With Singleton, the connection is created once and reused whenever needed.
object DatabaseConnection {
init {
println("Initializing database connection...")
}fun connect() {
println("Connected to database")
}
}
// Usage
fun main() {
val db1 = DatabaseConnection
val db2 = DatabaseConnection
db1.connect()
db2.connect()
println(db1 === db2) // true -> Both references point to the same instance
}
Key Takeaways
✅ Prevents Multiple Instances → Ensures only one object is created, reducing redundancy.
✅ Easy to Access → Provides a globally accessible instance without requiring manual instantiation.
✅ Thread-Safe in Kotlin → The object declaration handles thread safety automatically.
When to Be Careful
⚠️ Overuse Can Lead to Tight Coupling → If used excessively, Singleton can make components too dependent on a global state.
⚠️ Challenging to Mock in Tests → Since there is only one instance, testing or replacing it in unit tests may require additional setup.
This pattern is ideal for managing shared system-wide resources but should be used wisely to avoid making the code too dependent on a global state.
The Bridge Pattern is used to separate abstraction from implementation, allowing both to change independently. Instead of tightly coupling an abstraction to a specific implementation, this pattern introduces a middle layer that makes it easier to extend functionality without modifying existing code.
Example Use Case: Remote Control for Multiple Devices
A remote control should work with different types of devices, such as a TV, radio, or speaker. Without the Bridge Pattern, we would need separate remote controls for each device, leading to duplicated code. By applying this pattern, we can create a flexible remote control that works with any device.
// Common interface for all devices
interface Device {
fun turnOn()
fun turnOff()
}// Specific device implementations
class TV : Device {
override fun turnOn() { println("TV is ON") }
override fun turnOff() { println("TV is OFF") }
}
class Radio : Device {
override fun turnOn() { println("Radio is ON") }
override fun turnOff() { println("Radio is OFF") }
}
// Remote control abstraction
abstract class RemoteControl(protected val device: Device) {
abstract fun powerButton()
}
// Concrete remote control implementation
class BasicRemote(device: Device) : RemoteControl(device) {
private var isOn = false
override fun powerButton() {
if (isOn) {
device.turnOff()
} else {
device.turnOn()
}
isOn = !isOn
}
}
// Usage
fun main() {
val tvRemote = BasicRemote(TV())
val radioRemote = BasicRemote(Radio())
tvRemote.powerButton() // Turns TV ON
radioRemote.powerButton() // Turns Radio ON
}
A strong software system is built on solid design principles. At The Hong Kong University of Science and Technology, I explored essential aspects of System Analysis and Design that contribute to scalable and maintainable software.
📌 Architectural Design → Structures the system using loosely coupled subsystems to improve modularity and scalability.
📌 Class Analysis → Organizes objects into boundary, control, and entity classes for clear responsibility assignment.
📌 Class Design → Defines attributes, operations, and applies design patterns for better maintainability.
Spaghetti code is a term used to describe an unstructured, difficult-to-maintain codebase where functions and dependencies are tangled, making debugging and modifications extremely challenging. This often happens when a project evolves without proper architectural planning.
Example of Spaghetti Code in Kotlin:
fun processOrder(order: String) {
if (order == "Pizza") {
println("Processing pizza order")
} else if (order == "Burger") {
println("Processing burger order")
} else if (order == "Pasta") {
println("Processing pasta order")
}
// More if-else statements...
}
Solution: Implement modular design with structured methods or use Factory Pattern to simplify object creation.
A God Class is a class that has too many responsibilities, making the code difficult to manage and maintain. It violates the Single Responsibility Principle (SRP) by handling multiple concerns in a single unit.
Example of a God Class in Kotlin:
class OrderManager {
fun createOrder() { println("Creating order") }
fun processPayment() { println("Processing payment") }
fun sendNotification() { println("Sending notification") }
fun updateInventory() { println("Updating inventory") }
}
Solution: Break down responsibilities into separate classes, such as PaymentProcessor, NotificationService, and InventoryManager.
Refactored Code:
class PaymentProcessor {
fun processPayment() { println("Processing payment") }
}class NotificationService {
fun sendNotification() { println("Sending notification") }
}
class InventoryManager {
fun updateInventory() { println("Updating inventory") }
}
By applying Single Responsibility Principle, the system becomes more modular and maintainable.
Throughout my studies at The Hong Kong University of Science and Technology, I realized that mastering design patterns is key to writing scalable, maintainable, and efficient software. Applying them properly enhances software quality and prevents common pitfalls. What patterns do you use in your projects? Let’s discuss! 🚀