The ‘sweeping the floor’ pattern in chapter four pertains directly to the beginnings of an apprenticeship or just work in software in general. This pattern explains that when you first begin the process of working in software while you will not be nearly as knowledgeable or efficient as the other members of your team with more experience you will still be able to contribute in smaller or more simple ways in order to still provide assistance and work with your team as you learn how to do more technical tasks. This strategy can be applied in every field as when you start your career or even when you start a part time job in high school you first have to be taught how to do certain tasks in the correct way but while you learn how to do the more complicated tasks correctly you will still be able to put effort into making tasks easier for others who are already able to do the more complicated tasks that come with your job.

This pattern stood out to me as I believe that this is especially important to remember when you are entering any job or looking to start your career after college as the thought of ‘not knowing’ how to do the more technical tasks can be daunting for people at times as they see it as a massive obstacle you must overcome when in reality you are not expected to be able to do every task that more experienced team members are able to do. You are expected to contribute in any way you can to help your team work and provide insight while also remembering to continuously learn along the way in order to enable yourself to do these tasks in the future so that one day you are the ‘more experienced team member’ that someone looks to for guidance. Advice like this is important for college graduates that are just starting their careers as in most cases our first jobs will be learning experiences that we need to embrace and make the best of in order to further our understanding of both the field and our colleagues. 

From the blog CS@Worcester – Dylan Brown Computer Science by dylanbrowncs and used with permission of the author. All other rights reserved by the author.

This week’s blog will cover the main purpose of Object Oriented Testing and its usefulness. You most likely have heard the term “Object Oriented Programming”, which refers to a style of programming that utilizes classes, abstract classes, inheritance, polymorphism, concurrency, and more as a way of organizing code. These tools can be useful when dealing with multiple lines of code because it can be broken down into multiple files creating a more organized and readable product. Having a file with thousands of lines of code is a developers nightmare. Object Oriented Testing aims to test these systems and ensure they are behaving as expected. It is possible to have too much inheritance in a program, making it difficult to find where a piece of code is located, slowing down development. Unlike other test methods that primarily test function behavior, Object Oriented Testing analyzes the behavior of the entire class and its interactions with other files.

There are multiple techniques to Object Oriented Testing: Unit Testing, Integration Testing, Inheritance Testing, Polymorphism Testing, and Encapsulation Testing just to name a few. Unit testing refers to testing of individual components of the class before testing the interactions it has with other classes. Initially testing the classes functions will prevent scenarios where you can’t locate the bug in the program because there are too many inherited classes. An example is testing each function and ensuring the behavior. Integration testing refers to testing objects of different classes and ensuring they behave properly with all the components. Inheritance testing aims to test the relationship between parent and child classes. This technique of testing also tests overridden functions are properly implemented and are actually overriding the function. Polymorphism testing aims to verify that objects of different types can be used interchangeably. This type of testing ensures the behavior across all types of objects. Encapsulation testing tests access control and ensures the data being accessed is allowed to be accessed by the user.

The main benefits of running these tests is to detect defects early on rather than later in the development process. For this reason, it’s recommended to run tests throughout development. Object Oriented Testing ensures our project is modular, making it easier to maintain. In addition, it becomes easier to implement new features and classes without impacting existing code. Due to the never-ending demands of modern applications and the ever-evolving tech industry, the scalability of a program is crucial.

Blogs chosen: https://medium.com/@hamzakhan522001/object-oriented-testing-1f9619da40d0
and https://www.h2kinfosys.com/blog/object-oriented-testing/

From the blog CS@Worcester – Computer Science Through a Junior by Winston Luu and used with permission of the author. All other rights reserved by the author.

In the world of software development, ensuring code quality is paramount. It’s not just about writing code that works; it’s about ensuring that the code is robust, reliable, and maintainable. This is where various testing methodologies and tools come into play. In this blog post, we’ll explore three crucial aspects of code quality enhancement in JUnit: Smoke and Acceptance Testing, Exploratory Testing, and Static Analysis.

Smoke and Acceptance Testing: Smoke testing, also known as build verification testing, is a preliminary test that focuses on ensuring that the most critical functionalities of an application work. It aims to identify fundamental issues that might hinder further testing. Acceptance testing, on the other hand, evaluates whether the software meets the acceptance criteria and is ready for release.

JUnit, a popular testing framework for Java, offers robust support for both smoke and acceptance testing. Through annotations and assertion methods, developers can easily write tests that verify the functionality of their code. By running these tests regularly, developers can catch regressions early in the development cycle, thereby ensuring a smoother development process and a higher quality end product.

For further information on Smoke and Acceptance Testing, you can refer to the documentation here.

Exploratory Testing: Exploratory testing is a hands-on approach where testers explore the software application without predefined test cases. Instead, testers rely on their domain knowledge, intuition, and creativity to uncover bugs and issues. While automated tests are valuable, exploratory testing can uncover issues that automated tests might miss.

In JUnit, exploratory testing can be facilitated through the use of parameterized tests and dynamic test generation. These features allow testers to generate test cases dynamically based on various inputs, enabling thorough exploration of the codebase.

To learn more about Exploratory Testing, you can explore the documentation here.

Static Analysis: Static analysis involves analyzing the code without executing it, typically to find potential defects or code smells. In JUnit, static analysis can be performed using various plugins and integrations with static analysis tools like FindBugs, PMD, and Checkstyle. These tools analyze the codebase for issues such as potential bugs, code style violations, and performance bottlenecks, providing developers with valuable insights into improving code quality.

For detailed information on Static Analysis in JUnit, you can refer to the respective documentation of static analysis tools and plugins integrated with JUnit.

In conclusion, leveraging Smoke and Acceptance Testing, Exploratory Testing, and Static Analysis in JUnit can significantly enhance code quality, leading to more robust and reliable software applications. By incorporating these practices into the development workflow, teams can streamline the testing process and deliver higher quality code with confidence.

From the blog Discoveries in CS world by mgl1990 and used with permission of the author. All other rights reserved by the author.

After my week of spring break, I’m looking to get back to building constructive habits and reinforcing my software development fundamentals. I was inspired to return to this chapter of the “Apprenticeship Patterns” textbook because I recently learned about a computer science problem called the Circle-Ellipse problem.

This hypothetical problem is meant to highlight the limitations of the object-oriented programming paradigm, specifically when using subtype polymorphism in your class design. In the Circle-Ellipse problem, two classes are defined, Circle and Ellipse, with the Circle class inheriting from the Ellipse class. The Ellipse class includes two member methods, stretchX() and stretchY() which can modify, or mutate, two private member variables of Ellipse each representing an X and Y coordinate. The programming language understands all instances of the Circle class as also being instances of the Ellipse class with this inheritance relation. The problem arises when considering the requirements of a subclass. A subclass needs to implement all the member variables and methods of its parent. The Circle class needs to implement stretchX() and stretchY() as a subclass of Ellipse. Using either of these methods to change the dimensions of the Circle would consequently make that object no longer represent a mathematically correct circle.

As this problem was explained to me, the Liskov Substitution Principle was mentioned. I remember reading about this principle as one of the 5 SOLID software design patterns. The Liskov Substitution Principle essentially states that in a program using subtype polymorphism, an object ought to be able to be substituted by one of its subclasses without breaking the program. The Circle-Ellipse problem represents a violation of this principle because an Ellipse cannot be substituted for a Circle except in one very specific case.

I hadn’t considered the limitations of inheritance relationships in software design before learning about this problem. I decided that after the lecture where I learned about the Circle-Ellipse problem, and after seeing so many titles and authors name dropped in “Apprenticeship Patterns”, that I should start compiling a reading list and start more intentionally defining my daily schedule, including time for reading about software design and computer science. I started with a summary of the Circle-Ellipse problem, and then went through the appendix in “Apprenticeship Patterns” to choose a few books that the authors cited the most often.

The pattern introduces the possibilities of making your reading list public on the internet or using an existing list to source further reading material. The reader is also prompted to look ahead to the future and maintain the reading list to use for reflection on their learning journey.

From the blog CS@Worcester – Michael's Programming Blog by mikesprogrammingblog and used with permission of the author. All other rights reserved by the author.

The pattern of “The Long Road” highlights a common dilemma faced by aspiring software developers in today’s fast-paced industry. It contrasts the culture of instant gratification and superficial success with the desire to pursue mastery and craftsmanship in software development.

What intrigues me about this pattern is the disconnect between the long-term goals of becoming a proficient software developer and the short-term pressures to prioritize immediate financial gain and career advancement. Especially early in their careers, developers find themselves torn between these conflicting expectations while navigating through the complexities of the industry and trying to establish themselves.

This pattern resonates with me personally because it highlights the internal struggle I have faced in my own journey as a software developer. While I have not had any significant offers in the field of work yet, I know that true fulfillment in this line of work comes from the pursuit of excellence, continuous learning, and honing one’s craft over time. With my focus on excellence in my personal craft, it will pay off for me in the future later down the road.

The notion that the lessons and wisdom of seasoned software developers often goes unheeded in an industry that is constantly chasing the next big thing I found thought-provoking. The lack of knowledge transfer between generations of developers leads to the repetition of past mistakes and the reinvention of the wheel.

This pattern has caused me to reconsider my approach to my intended profession and how I want to work. Rather than falling into the external pressures and expectations, I am more committed to stay true to my passion for software development and prioritizing personal growth and mastery over short-term gains.

While I understand the advice to prioritize financial stability and career advancement, I do not completely agree with the notion that slow and steady skill-building is somehow less valuable or important. I believe that the pursuit of mastery is what ultimately sets exceptional developers apart and leads to long-term success and fulfillment in this field. I remain steadfast in my commitment to honing my skills, embracing lifelong learning, and striving for excellence in my craft, regardless of the trends or societal expectations.

3. Walking the Long Road | Apprenticeship Patterns (oreilly.com)

From the blog CS@Worcester – Jason Lee Computer Science Blog by jlee3811 and used with permission of the author. All other rights reserved by the author.

When you go to write code, maybe you already know what you want the code to do, knowing what it should give you as outputs and answers, but you might not know how to go about writing the code itself. In test driven development, you start with writing the tests first, and then trying to pass those tests, slowly as you go. It sounds like a strange approach to it but it is not as bad as it sounds.

In this blog post, Arek Torczuk talks about test driven development, and how it is the best thing that can happen to software development. First, they start off by describing test driven development, where they say it is, simply, a failing phase, a passing phase, and then a refactoring phase. In the failing phase, we create a test that will fail. In the passing phase, we create or write code, minimal code, that will pass the test. In the refactoring phase, we clean up the tests. Typically with coding, you would write the code, and then write tests. However, Torczuk presents some problems with this way of code development, like when to stop writing tests, or when you are sure implementation is finished. With test driven development, they say there are benefits, such as when writing tests, it makes you ask yourself, what do you want the code to do. They say that you should know the answers to these tests before the code is written. They then go on to provide situations where code may be impossible to test, and how test driven development can be used to help that, like using mocking. They provide code examples with these too, to further help visualize solutions. They provide some additional sources at the end of the article in the event you would like to learn more about test driven development. 

This is a strange way to write code, and for those used to writing code before everything else, this can be a difficult adjustment. Personally, I found it fairly difficult to start, but afterwards I was able to write the tests, and then write the code to pass those tests, and then keep doing that. The tests do provide a build up of the code, instead of doing everything all at once, which was what Torczuk was trying to get at with his blog post. It allows the tests to provide the framework of the code and lay it out. 

From the blog CS@Worcester – Cao's Thoughts by antcao and used with permission of the author. All other rights reserved by the author.