Link: https://codibly.com/blog/articles/yagni-how-to-do-things-when-you-actually-need-them-to-be-done

The blog post “YAGNI – how to do things WHEN you actually need them to be done” goes over the YAGNI (You Ain’t Gonna Need It) principle and why it is necessary as a guardrail against over-engineering in software development. The blog starts off by explaining the origins of YAGNI, as it originated from eXtreme Programming (XP) used in agile software development teams. Essentially, YAGNI should be used so that developers can resist the urge to implement features that are not necessary or needed. The blog compares YAGNI to KISS (Keep It Simple, Stupid), as while KISS advocates for more simplicity overall, YAGNI is more focused on discouraging unnecessary functionalities. The blog also goes over the risks of over-engineering, as it can lead to more bugs and simply be a burden when it comes to maintenance. Furthermore, it can also just lead to making your code way more complex for no reason. In the end though, in order to apply YAGNI in a responsible way it requires good judgement, as some additions are harmless as long as they don’t increase the complexity, but generally speaking it is better to keep it clean and simple.

I picked this blog post because I think that this is very important practice that will apply to a workplace environment. You always want to plan ahead and implement features that may be needed in the future, but overdoing it is not good. It is very important to find a balance, as doing too much of either can lead to big problems. For our course, this blog post also covers other software design principles, as well as some agile practices too. I think that all of these principles are very important for when it comes to working in a team environment, which is something that I will most likely have to do in the future. In a team environment, it is important to make sure your code is not complex, as other people will have to read it and potentially debug it as well.

Going forward, I plan on applying YAGNI principles to my current code as well as any code that I work on in the future. This blog gave me a good reminder that just because we might need an abstraction in the future, that doesn’t mean that we have to add it now. This can just lead to more maintenance, bugs, and just unnecessary complexity. I can apply these principles favoring simple versions of programs, as well as consistently reevaulating the requirements of a program. Overall, this blog post on YAGNI gives a great view and perspective on a principle that is very important in software design.

From the blog CS@Worcester – Coding Canvas by Sean Wang and used with permission of the author. All other rights reserved by the author.

The blog post/article I choose to read and write about is Mastering OOP Fundamentals with SOLID Principles from the ByteByteGo blog page. This blogpost goes into many aspects of the OOP programming, some of which we’ve discusses in class. The first portion delves into Encapsulation, Abstraction, Inheritance, and Polymorphism. It explores some key concepts like single inheritance, multiple inheritance, multilevel inheritance, hierarchical inheritance, method overloading, method overriding, etc. It explains how these 4 fundamentals are important for creating and utilizing OOP effectively, but in and of itself doesn’t necessarily create code that is easy to work with and maintain.

That’s why it introduces the principles of SOLID, which according to the syllabus is something we will eventually touch on in the future. The S is for Single Responsibility Principle, stating that classes should only have a single reason to change. This ensures better organization, easier debugging, and improved testability, so it’s better to split a complex class into multiple simplified classes. The O stands for Open/Closed Principle, stating that classes should be open for extension, but closed for modification. Essentially, if we want to add new behaviors to a class, new subclasses or interfaces should be added without changing what already exists. The L stands for Liskov Substitution Principle, stating that “objects of a derived class must be replaceable by objects of the bass class without altering the program’s correctness.” A subclass shouldn’t break existing functionality while behaving like it’s parent class. This somewhat relates to the interface portion of what we did in class. The I stands for Interface Segregation Principle, stating that clients should not be forced to depend on unused interfaces. Essentially, interfaces should be small and specific, with only relevant methods. Finally, the D of SOLID stands for Dependency Inversion Principle. This states that high-level modules and low-level modules should depend on abstractions, rather than each other. This can help improve flexibility and mobility, so that it’s easier to test and work with without making as many modifications.

The reason I chose this blogpost/article is because it directly relates to what we’ve learned with the fundamentals of OOP so far, as well as introducing me to something that’s planned for the syllabus. I also saw it as a good primer for Java and OOP thinking to help me better understand the general ideas and concepts that hold it up.

Even though a good portion of what was written is just reiterating some of what we’ve discussed in class, I found it really helpful to have things explained another way with the examples the blogpost gave. It helped me better grasp the purposes behind these fundamental principles and ideas in a way that felt easily digestible. The SOLID portion was also interesting, and everything intuitively makes sense. I can definitely see myself referring back to this and sticking to these ideas as I do OOP programming in the future, because it genuinely does seem to make the code easier to work with and easier to understand.

From the blog CS@Worcester – Site Title by Justin Lam and used with permission of the author. All other rights reserved by the author.

Hi, 

For this blog post I have chosen the topic design patterns in software construction. I watched the video linked below which is titled Design Patterns in Plain English | Mosh Hamedani by Mosh which does a great job of showing how design patterns in software construction work. https://youtu.be/NU_1StN5Tkk?si=aFdc2v01YIvoGq0m

Firstly, we must understand what exactly a design pattern is with respect to code. Design patterns are reusable solutions to common problems in code. From here we can make the inference that the goal of a design pattern is to build reusable and extensible software. To help us achieve this, there are three main categories which are creational, structural and behavioral patterns. Creational patterns regard different ways to create objects. Structural patterns are about the relationship between those objects. Behavioral patterns are about the interaction/communication between objects. From these categories we would choose which one to use based on the specific problem to implement the design pattern.

To help with the process of a design pattern, you would want to use a UML diagram to better visualize and understand the whole code. This way you will be able to see the type of relationships from one body to another; whether it’s inheritance, composition, and/or dependency to name a few. Something you may see on the UML diagram could be an interface.

An interface is a powerful tool that is often used in design patterns. Furthermore, they promote loosely coupled apps and flexibility. With an interface in the design pattern it strives substantially towards the overall goal which is to build reusable and extensible software.

Mosh then goes on to discuss and show the four principles of object-oriented programming, which are encapsulation, abstraction, inheritance, and polymorphism. Encapsulation deals with bundling data and the methods that operate on it into a single unit or class and hiding the values or state of an object within a class. In coding, abstraction is in regards to reducing complexities by hiding unnecessary details in classes. A great example of abstraction to better understand is thinking of a tv remote control; it shows only what you need to see. The next one is inheritance. Inheritance is a mechanism for reusing code across classes including common behavior. Try thinking of a parent to a child class. The child class inherits the parent class, but also has instances and methods of their own. Polymorphism is the ability of a single object to take many different forms. Mosh stresses that you must know and understand these principles as they are crucial to building design patterns.

Some takeaways are understand the problem you are facing first, have a strong foundation in OOP, and know when not to use a design pattern. Furthermore, creating multiple classes or a new interface just for one or two actions of a given state is simply not worth it; go with the else if statement.

From the blog CS@Worcester – Programming with Santiago by Santiago Donadio and used with permission of the author. All other rights reserved by the author.

I want to introduce a powerful tool in the arsenal of software engineers aiming to write clean, flexible, and maintainable code. The tool that most developers would need to upgrade the code without modifying the existing client code , and it is particularly helpful in scenarios involving algorithms of your code. The tool is called “Strategy Pattern“.

Strategy Pattern is a behavioral pattern that enables the selection of algorithms at runtime. This tool is crucial for developing flexible, maintainable, and modular code. Especially when multiple algorithms are applied to solve a problem. The key goal is to allow software entites be open for extension but closed for modification, meaning without having any impact or changed on the client code, but modifying and extending it.

Type of Strategy Pattern

1. Context – Holds a reference to a Strategy, delgates work to it. The Context doesn’t implent algorithm logic itself.
2. Strategy Interface – Defines a common method (or set of methods) that all strategies must implement, so they’re interchangeable.
3. Concrete Strategies: Classes implementing the Strategy interface, each providing a different algorithm implementation.

Benefits

1. Flexibility – New strategies can be added without modifying existing code.
2. Seperation of Concerns – Context is freed from algorithm details; each strategy handles its own logic
3. Easy to test: You can test each strategy class independently.

Disadvantage scenario

• Using pattern strategy creates more classes to manage, which can complicate desgin
• Some abstraction layers which may or may not be worth it in simpler scenarios.

Conclusion

The startegy patterns is useful when you have multiple algorithms or behaviors that needs to be added or swaped dynamically, in order to solve problem without impacting the existing code. It helps you build modular, maintainable, and extensible systems. But you should be mindful about the extra complexity it comes with.

From the blog CS@Worcester – Nguyen Technique by Nguyen Vuong and used with permission of the author. All other rights reserved by the author.

Polymorphism was one of those programming words that used to sound scary the first time I ever heard of it. It literally means “many forms” but in OOP, it’s not as scary-sounding as it is. It’s basically when the same method or function can be something different based on what object it’s being called from. As soon as I started reading over some examples, it made a lot more sense.

Think of it like this: an individual might be a class student, a home sibling, and maybe a work employee. Same person, various role depending on context. In programming, polymorphism enables us to do one thing with one function that changes its behavior depending on the object.

There are two main forms of polymorphism. Compile-time polymorphism (or static) is solved before the program ever runs. That generally happens with method overloading, where you have multiple versions on the same method name with different parameters. The compiler figures out which one to invoke. Operator overloading is another example, like using “+” for numbers and strings too.

Then there is runtime polymorphism (dynamic), resolved as the program is running. That is what happens with method overriding. A case in point is a parent class Shape that has a method draw(). Subclasses such as Circle and Square override draw() to do something else. When you call draw(), the program picks one that matches the actual object.

First of all, I used to confuse this with inheritance. They definitely go hand in hand, but they are not one and the same. Inheritance is when you are copying another class’s code. Polymorphism is when you have the same method name but have different action.

A lot of people also think that using polymorphism, especially the runtime kind, makes programs really slow. I used to believe that too, but it turns out that’s not really the case anymore. Modern compilers are built to handle this kind of thing quickly with tricks like virtual tables, so the performance hit is tiny. The trade-off is worth it because you get cleaner code and way more flexibility, so worrying about speed here feels kind of outdated.

I’ve read about this and it struck me how many times I’ve made my own code overly complicated. In Java, for example, I used to create a whole bunch of methods with unique names when I could have done overloading. I also blocked overriding in subclasses because I believed that it would be slow. Now I know that not just is that fine, it’s the correct thing to do most of the time.

In the future, I’d like to use polymorphism more intentionally, especially in my Android applications. It’ll be a tremendous assistance if new functionality is introduced in the future because I won’t need to begin anew. That, to me, is the beauty of OOP, it’s not just about making things function, but making them simpler to maintain and extend in the long run.

Resources:

https://www.cincom.com/blog/smalltalk/polymorphism-in-object-oriented-programming/

From the blog Maria Delia by Maria Delia and used with permission of the author. All other rights reserved by the author.

Polymorphism is one of those terms you hear in Computer Science that sounds confusing at first and overly complicated, until you find out you’ve probably been using it this whole time. “Polymorphism in Programming” by Johnathan Johnson is a great blog detailing exactly what it is and how it works. It gives a nice clear definition that’s easy to understand and then lists out the different types you’ll encounter along your programming journey.

Polymorphism is essentially just a process that can perform multiple tasks depending on the context of the situation. There are many forms of polymorphism as well such as Subtype (Runtime), Ad hoc (Compile-time), Parametric (Overloading), and Coercion (Casting). I won’t get too in-depth on each of these as Johnson has already done a great job of this in his blog.

The key takeaway here is that polymorphism allows you to program an interface, not just an implementation. What I mean is by creating an interface or superclass it doesn’t matter what type the object being called is you can just call the method on it. For example, you could have a list of Shape objects that can hold a circle or a square or whatever shape you want, and you can call draw() without caring which specific subclass you’re dealing with. Because of this code duplication can be severely cut down due to multiple objects using the same method all stored in the superclass.

This is similar to what we saw in class with the Duck superclass and all the subclasses with different duck types. As long as you were calling a method stored within the superclass it didn’t matter which subclass you were calling it on. Even if the subclass had overridden the method, you would just be running into ad hoc polymorphism as the program would be switching the version of the method used to that of the one stored in that particular subclass.

Even though I’ve been using polymorphism for years through my career here at Worcester State it’s good to finally learn what this practice is actually called. It’s also good to learn that there are forms of it I wasn’t always using when I could of and instead took the less efficient route to solve my problem. The concept is integral to time efficiency and when programming time is incredibly important in a lot of ways.

I plan on taking this newfound knowledge with me through the rest of my career here as well as whatever the future holds for me.

From the blog CS@Worcester – DPCS Blog by Daniel Parker and used with permission of the author. All other rights reserved by the author.