Category Archives: Technology

Connecting “Copyrights in AI” to Copyright and Licensing Homework

In the rapidly advancing world of artificial intelligence (AI), the intersection of technology and law has become increasingly complex. One of the most pressing legal issues is how copyright laws apply to AI-generated content. This is exactly what the article, “Copyrights in AI: Legal Overview” from HackerNoon offers, the author discusses the implications of copyright laws in the context of AI, focusing on whether AI can be considered an author of creative works, and how this impacts the rights of those who use AI to create content.

The article provides a clear overview of the current state of copyright law as it pertains to AI. Traditionally, copyright laws have protected works created by human authors, but with the rise of AI-generated content, it led me to ask: “can an AI be considered an author in its own right, or does the copyright belong to the human who programmed the AI, or the user who directed its output?” I learned that, under current law, AI cannot be considered an author in its own right, and the copyright typically belongs to the human creator or the user of the AI. This reflects a fundamental principle that we explore in our class, especially when considering software licenses. For example, when choosing a license for a software project, it is essential to understand the ownership of contributions and the rights of the contributors.

I selected this resource because the legal implications of AI are an area of particular interest to me, especially as AI continues to grow in influence and application across various industries. In one of my other classes, Computing Ethics, we talked about the ethical responsibilities and legal dilemmas surrounding the use of AI. The context being medical fields or business, how would the use of AI affect the users using it. This article connects those themes by highlighting the legal aspects of AI usage and authorship, which I had not fully considered before. It helped me understand that as AI technology becomes more sophisticated, the law may need to adapt to address new challenges.

By exploring “Copyrights in AI: Legal Overview” and reflecting on the licensing aspects discussed in my homework, I have gained a deeper understanding of how AI-related legal issues intersect with software licensing. In our Copyright and Licensing Homework, we focus on understanding different licensing models and the implications they have on the use and distribution of software so understanding who owns the rights to AI-generated works is critical to deciding how those works can be shared, modified, or distributed.

I expect to apply this knowledge when working with software projects, ensuring that the terms and conditions of any AI tools or systems used are clearly defined. As AI continues to grow in capabilities and its integration into software development increases, I believe this knowledge will be essential to navigating the complex legal landscape.

Link to the resource: HackerNoon article: Copyrights in AI: Legal Overview

From the blog SoftwareDiary by Oanh Nguyen and used with permission of the author. All other rights reserved by the author.

Discovering Design patterns

Hello Debug Ducker here again and have you ever thought about how your code is structured? I mean you probably have been doing simple code etiquette, but have you ever thought about how you could make it less say more manageable and neater to save yourself the trouble

Here is an example of a code design on UML from a programming assignment

The basic gist of this is that we are making ducks and applying qualities to them. As you can see there are different types of duck especially my favorite the rubber duck. But I am sure you can see a problem with this. Despite them being all ducks, not all the attributes of a duck can apply to certain ones as shown with decoy duck and rubber duck. Their quack and fly methods would be different, So we have to override them to do something else. This can get tedious especially if we were to add more ducks. Also makes the abstract class feel pointless because of this. So this is where Design Patterns are implemented

Instead of overriding the fly and quack methods in the different types of ducks, we add functions that can apply the behaviors themselves without needing to modify methods within ducks. The Relevant design pattern here is known as Strategy Pattern, and that’s when we get into the real meat of things. 

Design Patterns as the name suggests are designs that programmers can utilize to fix certain problems in their code, whether it’s readability, managing the code, or streamlining a process. Strategy Pattern is the design pattern that splits the specifics of a class into other methods, such as the example of the fly and quack behaviors which were originally a part of several other ducks with different qualities. This helps us whenever we want to add a duck with a different behavior, one of the behavior methods could be applied. There are several other design patterns out there such as factory design which creates objects through what called a factory method, for example, if the rubber duck method is made then an object with rubber duck qualities will be made

Here is code of an example of what a factory method would look like

There are a lot more patterns to choose from that can help you with all your coding problems. Geeksforgeeks has a great article explaining them and even more of the patterns to show

https://www.geeksforgeeks.org/software-design-patterns/

Design patterns can be useful for many coding problems, whether It’s to restructure your code to make working on it easier or refactor it to make the functionality better. I can see myself using theses whenever I would encounter a problem.

“Software Design Patterns Tutorial.” GeeksforGeeks, GeeksforGeeks, 15 Oct. 2024, http://www.geeksforgeeks.org/software-design-patterns/.

Guru, Refactoring. “Strategy.” Refactoring.Guru, refactoring.guru/design-patterns/strategy.

From the blog CS@Worcester – Debug Duck by debugducker and used with permission of the author. All other rights reserved by the author.

Is Open Source Best?

https://medium.com/chiselstrike/why-open-source-is-a-terrible-way-to-build-products-yet-most-great-products-use-open-source-c3bf9e201648

This post by Glauber Costa, an entrepreneur and former open source developer, discusses why open source is a horrible way to develop products but the best way to develop technology required for products. Technology and product are differentiated as product being something that uses technology to achieve some end market goal. I’ve always thought that open source was the best option for the vast majority of developers, with little exception outside of larger businesses where proprietary licenses would be best. This post challenged that notion.

When I look around the modern tech industry, I constantly see open source. I’ve known more people than I can remember who’ve used Godot, Shotcut, Krita, and Audacity; I’m writing this post on WordPress. However, the blog makes a great point: none of these products are better than their proprietary counterparts for general users. I always saw the community of open source developers and the possible self-customization of the works as the greatest strength of the licensing; I never for a moment considered how this lack of centralization in development is also Open Source’s biggest weakness. 

In ligth of this post, when I look around the Open Source world, I see very few products that my non-developer friends would ever use. For example, while I think Linux is great, I think all of them would prefer to stick with their Windows machines even if they had experience with Linux. I never thought to disconnect the impressive feats of community development as they pertain to the amazing innovations they’ve made to technology from the products that are released with said innovations, but in reflection, it makes perfect sense.

Open source development allows people to work on what they want when they want, and it gives individuals the chance to use their passion as an opportunity to solve problems they deeply care about. Proprietary development, on the other hand, gives people explicit goals aimed at creating the best end-user experience for everyone, with typically a strong centralization that ensures the product is polished, easy to use, and accessible to a wider audience. When you put it like this, it’s only logical that open source would result in the most innovative pieces of technology created by people with the drive to do so, while proprietary would result in the best products as they have an explicit focus to create something that would be best for the general user even if it comes at the loss of niche features for small groups of users.

From the blog CS@Worcester – CS ZStomski by Zachary Stomski and used with permission of the author. All other rights reserved by the author.

How Much Formatting is Too Much?

https://www.steveonstuff.com/2022/02/09/nitpicky-code-reviews-are-are-drag

This post from Steve Barnegren discusses his issues with the current state of team code review. Specifically, the blog takes time to point out the issues with being overly obsessive about nice formatting. It is one thing to point out flaws in logic and potential failures the system may have, giving feedback on how it might be improved or fixed, and another to, for example, say a ternary operator should be used instead of an if-else. The argument is made that a majority of ‘formatting issues’ of the variety I’ve given do not give enough value for the time they take between maintainers and developers.

Personally speaking, I like to have my code in a very consistent format. If just one thing is in a different format, it seriously bothers me. It wasn’t until recently when I started working on a project as a member of WSU’s Computer Science Club that I personally had to work in a team larger than two people, and in doing so I found out pretty quickly that many people do not hold the same standards to their code.

One team member very specifically does not care at all about how the code is formatted, focusing solely on efficiency and output. At the beginning of the project, I gave pushback on this, believing that the code he was writing was very poor if we wanted to maintain standards, but I was assured it wasn’t a big deal. All of us were working on different parts of the project and generally were disconnected from one another until it came time to connect things. I specifically was on my own creating the GUI of the project. However, the issue finally came when it was time for me to start working on the backend. What I found was a disaster, not in terms of functionality necessarily; there were definitely errors in output, but that wasn’t my concern. The real disaster was the cleanliness of the code. Trying to figure out what was going on, how things were calculated, what and where things were stored, it was a lot of tracing. 

By the time I finally understood what was going on, it took me very little time to do what was asked of me, but the process to get there should not have taken that long. The person who originally wrote the backend is now working to create extensive documentation so that way people don’t have to go through that process again. If there had been consistency in the formatting of the code, clear demonstration of how things functioned, and precautions taken to make sure things did not get out of hand, I feel it wouldn’t have been nearly as bad. 

Although I hear this blog’s thoughts, I hear them echoed in the person who originally said it wasn’t a big deal. In my opinion, the condition for a team to have code reviews like the one Steve recommends must be that all team members already agreed and showed the capability to write code that is clean and makes sense, or else you get the horror I had to go through. Generally, I think code reviews should be unique to every team, because the same rules don’t work for everybody, and that the nitpicks have their place in teams.

From the blog CS@Worcester – CS ZStomski by Zachary Stomski and used with permission of the author. All other rights reserved by the author.

Code Review

Source: https://about.gitlab.com/topics/version-control/what-is-code-review/

This article is titled “What is a code review?” As clearly stated by the title, the article explains the processes of code reviewing. “A code review is a peer review of code that helps developers ensure or improve the code quality before they merge and ship it.” Code reviews help in the identification of bugs, increase the overall quality of code, and enhance understanding of the source code. Code review, as suggested in the name, happens after a software developer has finished coding. Code needs to be checked before it is merged into an upstream branch for bugs or conflicts. A code reviewer “can be from any team or group as long as they’re a domain expert. If the lines of code cover more than one domain, two experts should review the code.” Adhering to a solid code review process allows for continuous improvement of code and aims to ensure that faulty code isn’t being implemented for customers/users to see and use. This process isn’t just important for the code itself, but also for all of the team members of a software development project. Whilst reviewing the code, meaningful knowledge of the source code is shared between team members to ensure that it is being implemented properly. The main benefits of the code review process are: the sharing of knowledge, discovering bugs earlier, maintaining compliance, enhancing security, increasing collaboration, and improving code quality. Code reviews allow for maintaining compliance because different developers have different backgrounds and thus different personal processes when they are developing. Code reviews allow these people to get together and maintain a standard coding style. Security is enhanced because “security team members can review code for vulnerabilities and alert developers to the threat. Code reviews are a great complement to automated scans and tests that detect security vulnerabilities.” There are many benefits to code review, but there are some disadvantages, including: longer time to ship, focuses being pulled from other tasks, and large reviews mean longer review times. These can be described as necessary evils due to the sheer amount of positives that code reviews offer in software development.

I chose this article because it was published by GitLab, a software that we are heavily using in class for version control, and I thought that it would be interesting to read this specific topic from the syllabus. Version control softwares such as GitLab allow code reviews to happen, so diving deeper into the topic in an article published by this popular software company was tempting. Before reading this article I understood that code reviews were important to pinpoint any bugs or difficulties before merging code into the upstream, but I never really thought about the implications of security or different development styles. I’ll definitely keep this information in mind during future code reviews on the job to remind myself that bugs aren’t the only important thing during a code review.

From the blog CS@Worcester – Shawn In Tech by Shawn Budzinski and used with permission of the author. All other rights reserved by the author.

YAGNI

Source: https://www.geeksforgeeks.org/what-is-yagni-principle-you-arent-gonna-need-it/

This article is titled “What is YAGNI principle (You Aren’t Gonna Need IT)?” YAGNI is “a principle in software development that suggests developers should only implement features that are necessary for the current requirements and not add any additional functionality that might be needed in the future.” The reasoning for this is that if you add features that might potentially be needed in the future, there will be risk for more bugs, increased complexity, and increased times of development, thus leading to increased cost. The YAGNI principle is similar to the KISS principle (Keep It Simple, Stupid), which also advocates for simplicity, it encourages developers to avoid complexity when it isn’t necessary. Developers should follow the YAGNI principle if they wish to keep the following costs in mind: the cost of building, delay, carry, and repair. The cost of building refers to the total cost of efforts and resources implemented in the project. Building things that aren’t needed leads to increased costs overall. Cost of delay refers to missed opportunities, if you spend time on unnecessary features, the development of more important ones will inevitably be delayed. Cost of carry refers to the difficulties of having unnecessary complex features. These complexities make it difficult to work on other parts of a software project, require more time, lead to an increased cost, and overall cause harder times moving forward. Lastly, the cost of repair, or technical debt, refers to the costs associated with bugs or mistakes that occur during the development process. YAGNI is important to ensure that the development process is focused, efficient, and cost-effective. YAGNI can be implemented into your code by prioritizing communication between team members. Ensuring that necessary requirements are met, a simple plan is made, ignoring ideas that don’t meet goals or deadlines, and keeping good records of project progress will allow your team to follow the YAGNI principle. YAGNI allows for simplicity, faster development, flexibility, reduced risk, and cost savings by complementing other development principles while prioritizing unnecessary implementations.

I chose this article because I appreciate how geeksforgeeks simplifies topics within the software development community. I don’t recall this principle being explicitly mentioned in class, but we have definitely alluded to it and I thought it’d be beneficial to read about it more, considering that it is in the syllabus. It was interesting to learn that the YAGNI principle complements other software development principles, such as the KISS principle, and compiles them into a unique principle that prioritizes simplicity over complexity and more features. It embodies the idea of “less is more.” This is a great set of guidelines I’ll be sure to follow in industry because it promotes that sometimes less work isn’t a bad thing. Instead of creating a multitude of features, ensuring that the ones that are critical, and required sooner, are being developed, will still get the job done.

From the blog CS@Worcester – Shawn In Tech by Shawn Budzinski and used with permission of the author. All other rights reserved by the author.

Workflow for a Developer


This week, I came across an post titled “Improving Developer Workflow” on Vercel’s blog, and it caught my attention because I’ve been trying to figure out how developers stay productive while coding. The article dives into different ways to make workflows more efficient, focusing on tools and practices that help developers ship better code faster. Since I’m new to computer science and still figuring out how to work effectively, this post felt super relevant to my learning journey.

The post highlights key aspects of improving developer workflows. It starts by discussing the importance of having fast feedback loops, meaning developers should quickly see the results of their code changes. This post introduces tools like Vercel’s platform, which makes it easy to preview, test, and deploy changes almost instantly. Another focus is on collaboration, emphasizing how tools like GitHub help teams share work and review code seamlessly. It wraps up by stressing the value of automation, like setting up CI/CD pipelines, to reduce repetitive tasks and ensure consistent quality in the codebase.

I chose this post because workflow optimization feels like an essential skill for any developer, even beginners. Sometimes I get stuck on repetitive tasks or wait too long to test my code changes, which can be frustrating. This post seemed like a good way to learn how experienced developers streamline their processes. Also, tools like GitHub and CI/CD were mentioned in class, so I wanted to understand them better.

The main thing I learned is how fast feedback loops can save a lot of time and frustration. For example, using tools like Vercel lets developers instantly preview their changes in a live environment, so they don’t have to guess if their code works. I also learned how CI/CD pipelines automate testing and deployment, which not only saves time but also reduces the risk of errors. I realized that these tools make a developer’s life easier, but they also require some setup and understanding, which I’m excited to learn more about. Another cool takeaway was how much collaboration matters in a developer’s workflow. I’ve used GitHub for simple projects, but the blog post made me realize how powerful it can be when teams use it for pull requests, code reviews, and tracking changes.

This blog post made me want to improve my own workflow by setting up faster feedback systems, even for small projects. I also plan to explore tools like GitHub Actions to try basic automation for testing. In the future, I hope to use these techniques to work more effectively on team projects and avoid common frustrations like repetitive tasks.

Resource:

https://vercel.com/blog/improving-developer-workflow

From the blog Computer Science From a Basketball Fan by Brandon Njuguna and used with permission of the author. All other rights reserved by the author.

A Microservice

A General Dive

This week, I looked into the topic of something called microservice architecture. It’s a way of designing software systems that breaks them into smaller, independent services that work together. To better understand it, I read the article “MicroserviceArchitecture”, which gave me a beginner-friendly explanation of how it works, its benefits, and some challenges. Since I’m new to computer science, I thought this topic was interesting because it seems like something I might use if I ever work on big software projects.

The article explains that instead of building one big application (a monolith), microservices split the application into smaller pieces. Each piece, or “service,” can do a specific job, like managing user accounts or processing payments. These services talk to each other using APIs, and they’re independent, so you can change or fix one without messing up the others. The article also talks about the pros and cons. On the good side, microservices make apps easier to scale and maintain. But on the downside, they can get pretty complicated to set up and manage.

I picked this article because microservices came up during class discussions, and I didn’t really get it at first. I wanted to find a resource that explained the basics without assuming too much prior knowledge. This article seemed perfect for a beginner because it explains things step by step. I also chose it because I’ve always wondered how big companies like Netflix or Amazon manage their systems, and it turns out they use microservices.

The biggest thing I learned is how microservices make scaling easier. For example, if one part of an app is getting more traffic—like a checkout service for an online store—you can scale just that service without touching the rest of the app. I thought that was really cool because it makes so much sense for big companies. Another thing I learned is how microservices make it easier for teams to work on different parts of a project at the same time. On the flip side, I also realized that microservices can be tricky because you have to make sure all the services work together smoothly. This made me think about how important it is to plan ahead when designing software.

I think this knowledge will be useful later when I work on group projects. If we ever build something complex, I might suggest using microservices to keep things organized. I also want to learn more about tools like Docker, which the article mentioned, because they help manage microservices.

Resource:

microservices-architecture

From the blog Computer Science From a Basketball Fan by Brandon Njuguna and used with permission of the author. All other rights reserved by the author.

AI Is Not A Software Engineer

In this blog, the author discusses how much the times have changed for new CS graduates. Reminiscing about how little they knew and how easily they got a job. Then talks about how much more prerequisite knowledge is needed to even sniff a job. The topic of the article is how now more than ever it is easier to get code that works. Thanks to AI, code is now more plentiful than it ever was before. However, all code is not good code. This leads to them discussing how despite how much code there is these days. Having people capable of understanding and able to build software are still very necessary. 

Although AI can now code for us, the coding wasn’t the hard part in the first place. The hard part was building software, and making good software. It’s easy to throw a bunch of code snippets together that accomplish something. But it is something entirely different to build specialized software that fills certain functions and meets certain criteria. AI cannot replace people, even though it may take away some jobs. At its heart, AI cannot build unique software. Teams of capable developers are still needed. The nature of how people code is changing. It’s becoming more important to be able to harness AI, but still oversee and build functional software.

I chose this article because I think it relates to team building. Like the article said, you need people who can understand code, not so much write it. Writing code is easier than ever, but finding people who understand how to build software is harder than ever. When using these tools it’s important not to rely on them too much. Discerning who can actually code these days is probably one of the most important skills for employers these days.  I think it’s important for me and everyone to keep in mind that AI is a tool. Tools dont make up for lack of knowledge. Tools are used best by people who know how to use them and maximize their use. One tool can’t solve every single problem. At the end of the day, knowledge is the most important part of being a software developer. 

Citations

https://stackoverflow.blog/2024/06/10/generative-ai-is-not-going-to-build-your-engineering-team-for-you/

By Charity Majors

From the blog CS@Worcester – Code Craft by Kyle Tucker and used with permission of the author. All other rights reserved by the author.

How AI Tools Separate Us From Information

It is no secret that ChatGPT has blown up recently. It is not just used by CS people, but everyone from all walks of life. It has become a common tool used to help people with a wide range of problems. Offering a quick way to get answers without needing to look for answers by yourself. However, these AI tools are not just a catch all solution for every problem. In this blog from Stack Overflow called “Knowledge-as-a-service: The Future of Community Business Models” discusses how these recent developments have affected how we access information.

In just the last twenty years alone, the way of searching for knowledge has changed. Going from books, to search engines, and cloud technology allowing for farther reach. In recent times we have seen the rise of AI tools that help guide us to the answers we seek. These AI tools however, create a separation between knowledge and the people who make it. AI does the searching and synthesizing for us. Although convenient, it raises the question if that is the best way for people to learn.

Some common concerns held by people are that ChatGPT offers answers. It often does provide context as to why solutions work. What works for one dev environment might not work in another. AI is also reliant on humans for new consumption knowledge. If humans are not creating new knowledge, AI cannot create new information. The credibility of these tools often comes under scrutiny as well. Many developers mention how much variance there is to answers. Although these are certainly draw-backs, developers are learning that community created content is more needed than ever.

I choose this topic because I believe that most students use ChatGPT or some other tool to help us. I myself use it often to help with pretty much every single class I take. But I definitely rely on it the most for CS. I ask how something works or what is the best course of action. I think it is a common concern for many employers cause many don’t know how to actually code. Many people just copy and paste without learning. I am guilty of this myself. But I have been working on trying to actually understand every bit of code. And learning of where and when to apply these code snippets I use. I believe it is still very important to learn from sources outside of chatGPT. Like from classes or other websites composed of trustworthy data. It’s good to learn how to do things yourself without relying on outside sources.

Citations

https://stackoverflow.blog/2024/09/30/knowledge-as-a-service-the-future-of-community-business-models/

By Ryan Polk and Ellen Bradenberger

From the blog CS@Worcester – Code Craft by Kyle Tucker and used with permission of the author. All other rights reserved by the author.