Application Program Interfaces (APIs) provide the framework for how pieces of software communicate with each other, as well as the user. Therefore, proper API testing is key in delivering a reliable product. In a blog for Medium.com, Andrey Enin says that around 3/4 of product functionality involves designing the API correctly. He also lists some of what he considers to be the core ideology behind how these tests are designed and conducted.

Firstly, an API, or any program, must be tested to ensure it works properly. Functionality testing involves sending requests to the API and comparing the results with the intended specifications. End-to-end tests are useful in this case; multiple calls are chained together, often across multiple APIs, to replicate the end user experience in order to bring bugs to the surface.

Performance testing is another point. Measuring how the software and API perform in high use cases or under high traffic loads improves stability by identifying issues in a controlled environment. That is, better to break it now, rather than after delivering the product. Compatibility testing falls under a similar umbrella. Differing and often “unexpected” requests are sent to the API to test how the program responds. While performance testing can involve stress-testing and compatibility testing often involves testing the API in different environments, the philosophy is the same: better for it to break now, when fixes are easier to implement as opposed to post-release.

In a similar thread is error testing. Quality software will be able to handle errors without suffering catastrophic failure. With API testing, this generally comprises of sending intentionally incorrect or malformed requests to ensure the interface returns the relevant error responses.

Testing the usability of the API is somewhat different, in that it refers to the developer experience when working with the interface. Some of the examples listed in the aforementioned Medium.com post include adhering to OpenAPI specifications, uniform naming conventions, and making sure documentation is comprehensive and unambiguous. The unique perspective of focusing on the end user experience helps create developer-friendly interfaces that are popular and remain relevant in the industry.

Finally, security testing involves checking to make sure the API carries data securely and is protected from unauthorized or malicious use. Transmitting data over HTTPS is a common method of protecting sensitive information, and is generally considered standard for most web traffic. Testing the interface’s authentication methods for vulnerabilities protects against outside access to the same sensitive information as well as preventing unauthorized use of the interface.

As a final note, it’s helpful to consider these testing approaches as a foundation for a standard of quality assurance, as opposed to a check list to follow. Specific tests may address multiple different parts, and different approaches may take importance over others. Considering each different aspect of API testing over the course of the development cycle, outside of the testing environment, helps ensure the quality of the final product.

References:

Enin, Andrey. “Field Notes in API Testing, Part 1: Areas of Focus.” Medium.Com, Medium, 25 Jan. 2025, adequatica.medium.com/field-notes-in-api-testing-part-1-areas-of-focus-46b516ccacf4#4439.

From the blog Griffin Butler Computer Science Blog by Griffin Butler and used with permission of the author. All other rights reserved by the author.

While reading parts of the book “Apprenticeship Patterns: Guidance for the Aspiring Software Craftsman” by Dave Hoover and Adewale Oshineye, there were a couple points that stood out to me when reading and comparing to my own experiences and knowledge.

An important point brought up by the book in the first chapter is how understanding the logic behind why things are done allows people to adapt to unfamiliar problems. Knowing the “spirit of the rule” allows for people to create new rules as needed. This is a point I personally feel strongly about. Knowing what to do is, really, only an issue of understanding instructions. Having a knowledge of the reasoning behind different practices allows you to apply that understanding to other problems.

Programming is a great example of this. It’s easy enough to follow a guide on writing code to follow some specific function, however, good developers will put effort into understanding how the code works, and will be able to replicate what they know later, or apply it to different situations. Someone who only knows the specific steps to writing the code doesn’t understand how it works, and most likely won’t be able to apply their experience outside of that specific use-case.

Another interesting point that I personally hadn’t considered was that apprenticeship in software development is much better approached as a mindset, rather than a formal form of schooling. The book describes that newcomers will have to “create their own opportunities for learning” in the working environments they find themselves in. I’ve understood that it’s important to have a desire to learn more than what may be expected, but the perspective of creating your own learning experiences, outside of more standardized curriculums but within the same environment was interesting.

This point also carries over into the beginning of chapter 3, where one of the authors describes how after connecting with other Perl developers, he was impressed not just by their knowledge, but by the speed at which they were still learning. Symbolically, the stacks of certificates and qualifications would become buried under book printouts and notes; the desire to learn more taking precedence over hard skills and qualifications that one can accumulate.

Both points culminate in the idea that being a great developer doesn’t mean having countless certifications or even being able to write code well. This is a good foundation, and great software developers usually fall into this category, but what makes them great is the understanding that there is more to learn and the desire to expand their knowledge and experience that sets them apart from the pack.

References:

Hoover, Dave H., and Adewale Oshineye. Apprenticeship Patterns Guidance for the Aspiring Software Craftsman Dave H. Hoover Aut; Adewale Oshineye Aut. O’Reilly, 2014.

From the blog Griffin Butler Computer Science Blog by Griffin Butler and used with permission of the author. All other rights reserved by the author.

Hello internet! This will be my blog for my CS-443 Software Quality Assurance and Testing class. I’ll be writing about things that I find interesting in the world of software testing, and I look forward to writing my first entry.

From the blog Griffin Butler Computer Science Blog by Griffin Butler and used with permission of the author. All other rights reserved by the author.

During the early 2000s, just as the dot-com bubble began to finally burst, a group of software development engineers came up with a new methodology for designing software. Previously, development teams used the waterfall development concept, in which each stage of development had to be completed in its entirety before moving on. While this process had worked well in fields such as mechanical engineering, the fluidity of the world of software development made this rigid structure inefficient.

The new process they came up with was known as Agile Software Development. This new methodology focused heavily on the idea that over the course of the development cycle, requirements are likely to change, unexpected bugs may cause issues, and functional code should be made frequently. This allowed for greater flexibility during development, as well as increasing collaboration between customers and shareholders with developers. This proved to be much more efficient and successful than waterfall development, and thus has become the industry standard.

However, Agile Development did not include the operations portion of a product’s life cycle. The operations teams are tasked with maintaining the infrastructure on which the product runs on, as well as deployment and management of the product. A lack of communication between the development and operations departments in the Agile structure could lead to operations teams being tasked with managing a product they have no insight into how it was developed.

This lack of transparency is addressed in a new method of development, known as DevOps. DevOps is designed to extend the principles of Agile Development to include both development and operations teams, allowing for both groups to communicate on how to design a product that works well for either party. To achieve this, DevOps often uses the CALMS framework; culture, automation, lean, measurement, and sharing.

Culture refers to the change in general mindset for teams. The idea is to shift towards a culture in which development and operations teams work together to increase efficiency. Automation of testing and development can increase quality of work and throughput of teams. Lean refers to the idea that failure is a key component to an experimentation, which is helpful in creating new and effective code. Measure ensures that test results are recorded and progress can be monitored and assessed. Finally, sharing refers to how collaboration and communication are essential components to DevOps.

While DevOps is still a new framework, it’s likely that it will be implemented in more and more development teams and companies. DevOps builds on existing principles laid down by Agile Development, while addressing the lack of transparency between development and operations. Because this is less of a radical shift in methodology and more of a modification of existing practices, it is likely DevOps will continue to grow and develop, just as Agile did 20 years ago.

Works Cited

Atlassian. “Agile vs DevOps.” Atlassian, http://www.atlassian.com/devops/what-is-devops/agile-vs-devops.

From the blog Butler Software Construction, Design, and Architecture by Griffin Butler and used with permission of the author. All other rights reserved by the author.

As I’ve discussed in previous blogs, the explosion of artificial intelligence in recent years has begun to revolutionize multiple aspects of life. The first things to come to mind, like self-driving cars, usually follow the pattern of taking a job that is normally performed by humans and automating it. There are tangible reasons for this; most people aren’t too interested in performing labor, so these are usually the first things that go to the drawing board for how AI can be implemented to ease human work. But what about something entirely based on entertainment and leisure?

The world of sports, obvious or not, is dominated by numbers and math. For every team of outstanding professional athletes, there is an equally outstanding scouting team, charged with finding the next big star. Or maybe a team uses analytics to find strengths and weaknesses in their opponents, as well as themselves. In more recent years, sports broadcasting companies have begun to integrate more statistics in their presentations.

While these jobs have always been possible, the implementation of machine learning into the process has caused the field to take leaps and bounds. For example, the NFL has partnered with Amazon to use their AWS Cloud Computing platform to deliver score outcome predictions, as well as more intricate stats, such as real-time pass completion probabilities. The utilization of services like AWS SageMaker has powered these “next-gen” stats and offers fans a different look at the game.

From a team management perspective, these new tools aid in keeping coaches and players informed about critical decisions. Imagine the position of a head athletic trainer. You have a player with an injury history who is scheduled to play in the upcoming game. Should you clear the player for the game, and risk an injury? Or do you sit the player, and risk a loss? These tough choices, while still difficult and heavily scrutinized, are now made easier with machine learning. The entire history of the player; injuries, performance stats, disciplinary records, can all be analyzed by AI to help coaches make an informed decision. Of course, these choices aren’t always correct, and hindsight is always crystal clear when looking at these mistakes. But with better number crunching, those mistakes are few and far between.

Finally, the use of machine learning in the role of officiating these sports has begun to emerge, with a healthy dose of controversy. Major League Baseball has experimented with using AI to track balls and strikes in addition to a human umpire. While this remains a hotly debated topic, and the jury is still out on whether or not these are changes that will catch on, sports continues to be transformed by machine learning, which may continue to revolutionize how we view the game.

Works Cited

“NFL next Gen Stats Powered by AWS – Stat That.” Amazon Web Services, Inc., aws.amazon.com/sports/nfl/.

Peranzo, Pete. “How Artificial Intelligence Is Transforming the Sports Industry?” Imaginovation | Top Web & Mobile App Development Company Raleigh, 23 Aug. 2022, imaginovation.net/blog/ai-in-sports-industry/.

From the blog Butler Software Construction, Design, and Architecture by Griffin Butler and used with permission of the author. All other rights reserved by the author.

In February of 2023, controversy struck when new Twitter owner Elon Musk announced that developers using the previously-free Twitter API would now be required to pay for its use. The cheapest package was announced to be $42,000 per month for access to 50 million tweets, which unsurprisingly has driven off an enormous amount of researchers and developers who can no longer afford to use the tool. In April of the same year, Reddit.com also announced that their API would no longer be free, charging $12,000 per 50 million requests. This decision too was met with intense criticism, as developers of multiple third-party apps were forced to shut down their services due to the cost.

Rather than trying to weigh in on this debate, let’s look at what an API is, why they are so essential for many of these third-party developers, and why they have quickly become unaffordable.

Firstly, an Application Programming Interface, or API, is an agreed-upon set of protocols that allow for the transfer of data between applications. It functions as an intermediary between systems, allowing for companies to open their application’s data to other pieces of software. This works by providing the framework for the external system to request specific data (known as a “call”), and receive the requested info (the “response”). Let’s look at Reddit for an example.

The Reddit API allows for third-party apps to request and receive data such as posts, comments, videos, etc. to be used on their own application. The app RedReader, for example, pulls information from Reddit and displays it in a way that is more accessibility-friendly than the normal site. To do this, the app must make calls for data to be sent from Reddit servers, which it uses to update its own app.

Here lies the debate regarding paid vs. free APIs; they provide the valuable ability to integrate proprietary software with external applications, the controversy surrounds how valuable this ability actually is. On one side, an API should ideally be profitable for the company designing it to offset the cost of development and to monetize the use of the company’s data. On the other hand, the trend of extremely high prices set by platforms such as Twitter and Reddit have kicked out many smaller development teams, especially those who use the data for non-commercial work. In an interview with Wired, Kenneth Joseph, an assistant professor at the University of Buffalo, explained that his work of analyzing how people use Twitter has effectively been ended by the eye-watering costs of use.

The correct answer to the question of how valuable an API is can often depend entirely on which side of the argument you’re on: company executives will state that the use of their data is something that cannot be provided for free, while developers and researches are adamant that such extreme pricing has made their use unaffordable. In the future, as the amount of available data continues to grow, this debate is surely to come around again.

Works Cited

Binder, Matt. “Twitter’s API Keeps Breaking, Even for Developers Paying $42,000.” Mashable, 29 June 2023, mashable.com/article/twitter-api-elon-musk-developer-issues-apps.

IBM. “What Is an Application Programming Interface (API) | IBM.” Www.ibm.com, 2023, http://www.ibm.com/topics/api.

Red Hat. “What Is an API?” Redhat, 2 June 2022, http://www.redhat.com/en/topics/api/what-are-application-programming-interfaces.

Roach, Jacob. “Why Everyone Is Freaking out about the Reddit API Right Now.” Digital Trends, 14 June 2023, http://www.digitaltrends.com/computing/reddit-api-changes-explained/.

Stokel-Walker, Chris. “Twitter’s $42,000-Per-Month API Prices out Nearly Everyone.” Wired, 10 Mar. 2023, http://www.wired.com/story/twitter-data-api-prices-out-nearly-everyone/.

From the blog Butler Software Construction, Design, and Architecture by Griffin Butler and used with permission of the author. All other rights reserved by the author.

Automation is an often-overlooked concept when it comes to the study of how human civilization has evolved, which, is a shame, since it has had such a crucial role in making the modern world. The invention of farm equipment meant agriculture required less labor, allowing people to focus their lives on education. These people would go on to create further innovations, creating a cycle in which people create new technologies that give subsequent generations more opportunities to design their own creations.

The automotive industry is a great modern example. Several decades ago, car production plants employed millions, while now, these same factories almost exclusively use robots for the manual labor tasks previously delegated to humans. On the one hand, this has many benefits. Cars are now easily affordable to the regular person and production is now streamlined and faster. On the other hand, however, those jobs working on the factory line are gone, and those previously working on them are left without work.

As artificial intelligence explodes into the modern world, this same process is already beginning to take place. According to Yahoo Finance, as of July 2023, almost a quarter million employees working in the tech sector have been laid off due to being replaced by AI. The idea of replacing expensive employees with comparatively free AI labor is an extremely enticing opportunity for business executives. Just as robots replaced human workers on assembly lines, AI programs have begun to replace human workers sitting in the office.

This is not a phenomenon exclusive to the tech sector either. If you’ve ever had to talk on the phone with customer service for any large company, the voice you first hear after dialing often is no longer a human, but rather a computer. By using voice-to-text software powered by machine learning, calls can be filtered by issue type, and automatically directed to the proper department, where a human can address the caller as required. Sometimes, you won’t even talk to a person at all.

For the sake of clarity; the advancement of machine learning will not lead to artificial intelligence replacing the entire human workforce. Remember back to the previous point about how automation led to the loss of jobs in the automotive industry? This also led to new jobs being created, from designing and programming robots to assemble cars, to technicians maintaining these robots when they inevitably break. The same can be said for artificial intelligence. While self-driving cars may some day replace taxi drivers, this would also lead to job openings in developing and maintaining the software behind them.

The idea that AI will replace humans completely is a scary pitfall to get into, however, this isn’t a situation that humans haven’t experienced before. As technology grows and develops, so does society. Usually for the better.

Works Cited

Bizouati-Kennedy, Yael. “How AI Is Already Causing Layoffs in the Tech Industry.” Yahoo Finance, Yahoo, 7 July 2023, finance.yahoo.com/news/ai-already-causing-layoffs-tech-181221028.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAJAmzoKggZHQoJa3SOThKQQJGXifMZImGxyqNzN0QuSfct2VP3Hogm4jAVSBaWYVCO3qG2EFiZ3w9vvVmynD1s7Z-Azpd6vHXVAQGqJzbb8F49e13O-O_d13SdyEm5vAzsdi3hQ3Pni6tJrWmh3Y64m4Fzd_igLHjv54uf8kMykf.

Kothari, Jash. “Artificial Intelligence: Cause of Unemployment.” GeeksforGeeks, 16 Aug. 2019, http://www.geeksforgeeks.org/artificial-intelligence-cause-of-unemployment/.

“Will AI Replace Humans in Customer Service?” Www.ultimate.ai, http://www.ultimate.ai/blog/customer-experience/will-ai-replace-humans-in-customer-service#:~:text=It.

From the blog Butler Software Construction, Design, and Architecture by Griffin Butler and used with permission of the author. All other rights reserved by the author.

To say that the use of containers has revolutionized how applications are designed and deployed would be an understatement. Gone are the days of applications being run on physical servers, as due to the issues with resource management, the alternative of using virtual machines to run multiple applications on a single CPU provides vastly more flexibility to developers. One downside of virtualization, however, is that these virtual machines are considered rather “heavy.” Each VM is a fully-functional machine, running a full OS in addition to whatever virtualized hardware is added on. In environments where each server’s CPU may have multiple virtual machines running, the same issue occurs; high resource usage.

To solve this problem, the use of containerized software has become common. Containers share the same OS instance as the host machine, as opposed to a VM, which has an entirely separate OS. This leads to a multitude of benefits, such as reducing image storage size. While a VM uses images multiple gigabytes large, container images are much smaller; often measured in megabytes. Additionally, containers are entirely independent, meaning they are much more easily portable, which leads to faster and easier deployment.

One major drawback to containers is the upkeep. Containers must have a specific amount of resources added to them, such as memory usage, while also, like anything else, can fail and need repair.

Enter Kubernetes; an open source platform designed to automatically conduct these maintenance tasks. Kubernetes uses clusters as the basis for their infrastructure, each containing nodes which run and manage the application. Control plane nodes manage scheduling, the API server, and other services. Worker nodes are where applications are actually run, with larger applications using more worker nodes than smaller ones.

If such a platform is so valuable, why are so many positions left unfilled? Because jobs working with Kubernetes are hard.

On the one hand, developing and maintaining applications with Kubernetes requires experienced engineers, and time. The nature of the environment simply demands developers have the knowledge and experience to implement it.

On the other hand, because Kubernetes is such a new technology, the field is rapidly evolving, requiring developers to evolve along with it. Each change requires testing and optimization, as well as programmers needing to continue to broaden their expertise.

Why is this problematic? In addition to creating a work environment prone to causing burnout among employees, engineers working in this field can outgrow their positions quickly. As they gain more experience and widen their skillset, many move on to positions that offer higher pay. According to Forbes, Kubernetes engineers spend an average of just 18 months in their positions before moving on.

This creates a cycle; engineers are hired to work on Kubernetes platforms, hold their positions for a short while, and either due to the intense workload, higher paying positions, or a mix of both, move on. This leaves an opening in their previous position, which must be filled by a new hire. Rinse and repeat.

Works Cited:

Budhani, Haseeb. “Council Post: Addressing the Kubernetes Skills Gap.” Forbes, http://www.forbes.com/sites/forbestechcouncil/2023/05/10/addressing-the-kubernetes-skills-gap/?sh=2a1430223f42. Accessed 20 Nov. 2023.

“Overview.” Kubernetes, kubernetes.io/docs/concepts/overview/. Accessed 19 Nov. 2023.

Poulton, Nigel. “What Is Kubernetes, and Why Should I Learn It?” Www.pluralsight.com, 2 Jan. 2023, http://www.pluralsight.com/blog/cloud/what-is-kubernetes#:~:text=Kubernetes%20is%20notorious%20for%20having. Accessed 20 Nov. 2023.

From the blog Butler Software Construction, Design, and Architecture by Griffin Butler and used with permission of the author. All other rights reserved by the author.

The world of Computer Programming is one that is comprised of people from every corner of the globe, from countless different social and economic backgrounds, which has led to one of the most diverse fields of study in the world. Some come from well-established foundations; utilizing the resources at their disposal to create new and innovative products. Many more come from more humble beginnings, forging their own path and transforming the world around them. Yet, across this wide spectrum of developers, researchers, and teachers, they all share one thing in common:

At some point in time, at the beginning of their studies, they had relatively no idea what they were doing.

Sure, this sounds harsh. But it’s true for quite literally every field of study imaginable. No one is born with the ability to write machine learning algorithms, develop web applications, or any other kind of work in the field. At one point or another, this knowledge was learned, practiced, and perfected.

In the world of computer programming, the amount of seemingly trivial yet incredibly difficult problems one may encounter can be quite high. They can often be hyper-specific issues where directly posing the question at hand to your peers may be the most effective way of coming to a solution. And in the modern age of the internet, an incredible medium of getting answers proves time-and-time again to be a rock-solid support for developers: StackOverflow.

StackOverflow is a forum made for programmers to post issues and questions related to their work, and to receive answers from other programmers on how to resolve these issues. It can be astonishing how regardless of how niche or specific you think your issue may be, there’s an extremely good chance that someone else had the same problem, posted it, and received multiple different solutions. StackOverflow has the benefit of being powered by the vast workforce of the internet, where millions of people can chime in and offer their assistance. However, therein lies the problem:

The internet can be full of jerks.

Of course, I don’t mean to discredit the vast majority of users who are often incredibly helpful and welcoming to newcomers. The usefulness of StackOverflow and similar sites cannot be overstated. The core issue, in my opinion, is the vocal minority who either through arrogance or simply a lack of tact, can scare away those new to the programming field with a sarcastic attitude and an aura of superiority.

This isn’t just anecdotal either. Nate Swanner from Dice.com writes about how in 2019, after StackOverflow made efforts to convince users to “be nice” to each other, the annual developer survey reported that 73 percent of users saw no change in how welcoming the site was to new users. He acknowledges that while the vast majority of users are able to find answers to their questions, there exists a problem in how the forum treats its fellow developers.

“When you’ve got to wade through a river of ego and spite before being told to ‘Google it,‘ we start to wonder how long people will tolerate a Stack Overflow where a “cultural shift” hasn’t yet taken hold.

While one of my least-favorite things to do is to harp on a problem without offering any kind of solution, I really don’t believe there is one singular fix to this kind of issue. After all, this isn’t a phenomenon exclusive to StackOverflow, or even the Computer Science field for that matter. Sometimes, when we hear a question from someone that to us sounds trivial, our knee-jerk reaction is to think “Dude, really? THAT’S your issue?”

This is the mentality that I believe should be addressed and challenged more often in order to create a more welcoming community. The field of computer programming is growing fast. And there are definitely indications that we’re moving in the right direction. That same developer survey found that users on StackOverflow who were people of color felt more welcome than in previous years, which is a great step forward. However, fields of study are driven by the people who study them. And the more welcoming and supportive these groups are, the more a field will grow.

A quick side note: I hope this post didn’t end up being too philosophical; I had originally planned to write about a completely different topic, however after hearing about some experiences from a friend of mine who has just started his freshman year in a CS program, I felt that this was a topic worth discussing.

Citations:

Nate Swanner. “It’s Not Just You: Stack Overflow Is Still Full of Jerks.” Dice Insights, Dice, 18 Apr. 2019, http://www.dice.com/career-advice/stack-overflow-many-jerks.

From the blog Butler Software Construction, Design, and Architecture by Griffin Butler and used with permission of the author. All other rights reserved by the author.