Using Rest API in class while working with our in-class and homework assignments has helped me understand backend development in a practical way. Vaibhav Kandwal’s blog, “REST API Tutorial – REST Client, REST Service, and API Calls Explained With Code Examples” does a really good job of simplifying and giving a top-down overview of what it looks like. Throughout my semester in Software Construction, Design, and Architecture I’ve learned a lot about Rest APIs but Kandwal’s way of breaking it down has deepened my understanding further.

One of the biggest points made in the blog is that Rest APIs are built around the idea of resources. Instead of being stuffed full of complicated actions, everything instead becomes a resource that can be created, retrieves, updated, and deleted. This makes endpoints easy to understand as when you see something like /guests you know what to expect. GET gets the data, POST adds something new, PUT or PATCH updates it, and DELETE removes it. REST reduces the guesswork by having a consistent structurer to follow.

The blog also heavily emphasizes that REST APIs are stateless. I wasn’t entirely sure why this was the case at first but after reading through it makes sense now. “Statelessness: the communication should have no client context stored on server. This means each request to the server should be made with all the required data and no assumptions should be made if the server has any data from previous requests.” The reason this is so important is scaling becomes much easier. The server doesn’t have to store anything about the previous requests. This also has the added benefit of system reliability. When the server doesn’t have to keep track of any previous conversations, there are fewer opportunities for unexpected bugs. This creates a safer and well-organized approach.

This connects directly with what we’ve worked with in class. One of our more recent homework assignments was created the endpoints in the Specification.yaml file and it was easy to see how each one followed the same structure with minor differences depending on the requirements for that endpoint. We decide what our resources are, what each endpoint should do, and what kind of data each operation should accept or return. Having that structure laid out before me has increased my understanding of the backend logic and made it much easier when it finally came time to write the JavaScript.

Overall, after reading this article I have a deeper understanding or REST APIs, and I plan to take this with me into my future career. I will certainly need this knowledge next semester when I take my Software Development Capstone course but also after I graduate seeing as how I have my sights set on being a software developer.

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

The single responsibility principle is simple but critical in good software design. As Robert Martin puts it in his blog The Single Responsibility Principle, “The Single Responsibility Principle (SRP) states that each software module should have one and only one reason to change.” He also does a great job of comparing this to cohesion and coupling, stating that cohesion should increase between things that change for same reason and coupling should decrease for those things that change for different reasons. Funnily enough, while I was reading a post on Stack Overflow I ran into this line, “Good software design has high cohesion and low coupling.”

Designing software is complicated, and the program is typically quite complex. The single responsibility principle not only creates a stronger and smarter structure for your software but one that is more future-proof as well. When changes must be made to your program, only the pieces of your software related to that change will be modified. The low coupling I mentioned earlier will now prevent the possibility of breaking something completely unrelated. I couldn’t count the number of times my entire program would break by modifying one line when I first started coding, because my one class was doing a hundred different things.

This directly relates to what we’re working on in class right now. We are currently working with REST API, specifically creating endpoints in our specification.yaml file. Our next step will be to implement JavaScript execution for these endpoints. When we begin work on this keeping the single responsibility principle in mind will be incredibly important. It can be very easy to couple functions that look related but change for completely different reasons. For example, coupling validating new guest data and inserting the new guest into the database. While they seem very related, they may change for very different reasons. Maybe validation requirements change, causing the validation process to be modified but not the inserting of a new guest. The database structure or storage system may change leading to modifications to how the new guest is inserted but not how they’re validated. Keeping in mind that related things may change for different reasons will be key for my group leading into the next phase of our REST API work.

This principle is one that I plan on carrying with me during my career as a programmer. It will help me create more future-proofed programs in a world where things must be ready to constantly evolve and adapt. Uncle Bob’s blog was incredibly useful in my understanding of this principle on a deeper level. I feel like a stronger programmer after just reading it. I look forward to implementing what I’ve learned as soon as we start working with the JavaScript of our REST API.

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

In my software construction, design, and architecture class we’ve focused a lot on (believe it or not), the design and structure of software. Software is typically quite intricate and designing it as efficiently and cleanly as possible is almost never accomplished the first time it is coded. “Code Refactoring and why you should refactor your code” by Lazar Nikolov is a great blog dedicated at explaining what refactoring is, why you should use it, and when you should use it.

Refactoring is just the process of identifying technical debt and code smells withing the existing program and modifying the code to be more optimized removing these issues without changing the user interface behavior. As Nikolov describes there are many objectives of refactoring such as increasing readability, maintainability, reusability, optimizing the performance, and enforcing code standards. There are many situations you should consider refactoring in. Such as DRY where you find and replace repetitive information with an abstraction that is less likely to change, or when working with someone else’s “bad” coding that you need to build on.

The key takeaway here is that no programmer does the job perfectly the first time. Programs constantly evolve and have changing specifications that must be met. A good programmer is one who can roll with those punches and change the code as needed. Our most recent homework assignment really focused in on this and had us refactor the code we we’re working with three different times each time using a different architecture. In doing so we learned each time how the previous code could be improves upon to function better and with less bugs even though the original code worked fine. Moving from the strategy pattern to the singleton pattern lastly to the simple factory pattern allowed us to see how each new version after refactoring solved a different problem from the previous. Even though they all worked fine we could see the optimization occurring.

Refactoring is certainly a concept I plan on carrying with me into the future. I’m not sure if there’s a good programmer out there that doesn’t use this concept constantly. Having the ability to constantly adapt my code to best suit it to not only its current task but any future ones that may arise is an incredibly good skill to have. I know it will help me not just as a student at Worcester State but as a professional software developer someday too.

From the blog CS@Worcester – DPCS Blog by Daniel Parker 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.