Summary:

The “Practice, Practice, Practice” pattern emphasizes the importance of deliberate practice and continuous improvement in mastering any skill, particularly in the realm of software development. Drawing from George Leonard’s concept of mastery and K. Anders Ericsson’s research on deliberate practice, the pattern highlights the necessity of carving out dedicated time for practice separate from daily professional responsibilities. While an ideal scenario involves structured exercises and mentorship, the reality often requires individuals to take initiative in their own skill development.

Reaction:

This pattern strikes a chord with me as it underscores the essence of lifelong learning and skill refinement. The notion that mastery is not merely a destination but a journey fueled by deliberate practice resonates deeply. It challenges the notion of perfection in favor of embracing imperfection as a catalyst for growth. Furthermore, the emphasis on creating a stress-free and playful environment for practice aligns with my belief in the importance of experimentation and exploration in learning.

Interest and Utility:

What I find intriguing about this pattern is its application of martial arts principles to software development. The concept of code katas, akin to choreographed movements in martial arts, offers a structured framework for practicing fundamental coding skills. Moreover, the emphasis on short feedback loops and the integration of public performance within a community of craftsmen underscores the collaborative nature of skill development in the tech industry.

Impact on Professional Outlook:

As someone aspiring to excel in software development, this pattern has prompted me to reconsider my approach to skill acquisition. Instead of viewing mistakes as setbacks, I now perceive them as invaluable learning opportunities. By committing to regular practice sessions and seeking feedback from peers, I aim to cultivate a growth mindset and continuously refine my coding abilities. Additionally, the suggestion to explore timeless resources like “Programming Pearls” for practice exercises has inspired me to delve deeper into the fundamentals of computer science.

Disagreement:

While I agree with the overarching principles of the “Practice, Practice, Practice” pattern, I believe there’s a need for acknowledgment of individual learning preferences and constraints. Not everyone thrives in a structured dojo environment, and some may prefer solitary practice or alternative forms of skill development. Therefore, while code katas and group sessions offer valuable avenues for improvement, they may not be universally applicable or accessible to all aspiring developers.

Conclusion:

In conclusion, the “Practice, Practice, Practice” pattern serves as a poignant reminder of the importance of intentional practice in achieving mastery in software development. By embracing the principles of deliberate practice, seeking feedback, and exploring diverse learning resources, individuals can embark on a journey of continuous growth and skill refinement. As I incorporate these insights into my own professional development, I’m excited to see how regular practice and reflection will shape my journey toward mastery in software engineering.

From the blog CS@Worcester – Site Title by rkaranja1002 and used with permission of the author. All other rights reserved by the author.

As a student of computer science, encountering different tools that streamline coding processes is a cornerstone of my educational journey. One such tool that has caught my attention this semester is pytest, a testing framework for Python that allows for simple unit tests as well as complex functional testing. After a thorough exploration, here’s why I believe every budding software developer should dive into pytest’s documentation.

Why I Chose This Resource

The reason I chose to delve into the pytest documentation is twofold. Firstly, our course has increasingly emphasized the importance of test-driven development (TDD), a method pytest excels at supporting. Secondly, several industry professionals I look up to have recommended pytest for its simplicity and efficacy, making it an essential skill in a developer’s toolkit.

Summary of the Resource

The pytest documentation provides a comprehensive guide to getting started with pytest, from installation to writing your first test. It covers key features like fixtures for a scalable and modular setup, markers for categorizing tests, and plugins to extend pytest’s capabilities. The documentation is well-organized and rich with examples, making it accessible to newcomers and a valuable reference for experienced developers.

Personal Reflection and Application

Reading through the pytest documentation was an enlightening experience. It not only clarified the mechanics of pytest but also underscored the benefits of using such a tool in real-world programming. One of the standout sections was on ‘parametrized testing’, which illustrated how to execute multiple permutations of a test with different input sets, ensuring broader coverage with fewer lines of code.

This resource has profoundly affected my approach to programming. It has instilled a more disciplined mindset towards testing, making me appreciate how early detection of issues can save time and resources in the development cycle. I now plan to integrate pytest into my upcoming projects, confident that it will enhance the quality and reliability of my code.

Future Practice

The knowledge gained from the pytest documentation is something I intend to apply in all my future software development endeavors. I see it as a step towards adopting best practices in testing, which is vital for any aspiring software engineer dedicated to producing robust and fault-tolerant software.

Conclusion

For any student of computer science, understanding the tools at your disposal is as crucial as mastering programming concepts. The pytest documentation is a goldmine of information that promises to elevate your testing skills. I highly recommend it to anyone looking to embrace test-driven development fully.

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

What is JUnit?

JUnit is an open-source framework used for automated unit testing in Java. Unit testing involves testing small, isolated units of code to verify that they function as expected. These units could be individual methods, classes, or even a line of code.

When developers write code, they also write tests for that code using JUnit. These tests are then run automatically, checking for any deviations from expected behavior. JUnit provides feedback in the form of pass or fail results.

Why Unit Testing with JUnit?

Unit testing, facilitated by JUnit, offers several benefits:

1. Early Bug Detection: By writing tests before or alongside the code, developers catch bugs early in the development process. This ensures that issues are addressed promptly and doesn’t propagate further.
2. Code Reliability: Writing tests encourages writing modular, well-structured code. This leads to more reliable and maintainable codebases.
3. Confidence in Changes: With a comprehensive test suite, developers can refactor or modify code confidently, knowing that any breaking changes will be immediately flagged by failing tests.

Features of JUnit

JUnit offers several features that make it popular among developers:

1. Open Source: Being open source, JUnit is freely available for developers to use and contribute to. This fosters a vibrant community and ensures continuous improvement.
2. Annotations: JUnit provides annotations to identify test methods, setup, teardown, and more. These annotations make test code more readable and maintainable.
3. Assertions: It includes a set of assertion methods to validate expected outcomes. These assertions help in comparing actual results with expected results.
4. Test Runners: JUnit has test runners that execute tests and report the results. It supports different types of test runners for various testing scenarios.
5. Automated Test Running: Tests run automatically without manual intervention. This saves time and effort and provides quick feedback on the code’s health.

JUnit Annotations

Annotations in JUnit provide metadata to methods to control their behavior during testing:

• @Test: Marks a method as a test method.
• @Before: Executes before each test method.
• @After: Executes after each test method.
• @BeforeClass: Executes once before any test method in the class.
• @AfterClass: Executes once after all test methods in the class.

Conclusion

JUnit is a vital tool for Java developers, ensuring code quality, reliability, and maintainability. Its features, including annotations and automated testing, make it invaluable in modern software development.

By understanding JUnit and incorporating it into your workflow, you can write better, more robust Java code. Embrace automated testing with JUnit, and watch your code quality soar!

Source: https://www.simplilearn.com/tutorials/java-tutorial/what-is-junit

From the blog CS@Worcester – CS: Start to Finish by mrjfatal and used with permission of the author. All other rights reserved by the author.

Summary: “Expose Your Ignorance” advocates for honesty and humility in software development by encouraging developers to openly admit what they don’t know and actively seek learning opportunities. The pattern emphasizes that expertise is a result of the learning journey, not the end goal, and that building trust within teams is essential for growth.

Reflection: I really resonate with the idea of embracing ignorance as a natural part of learning, especially in an industry where it feels like you need to know everything. Admitting what we don’t know can be tough, but I’ve learned that it actually creates a better environment for learning.

Embracing Vulnerability: Admitting that we don’t know something can make us feel vulnerable, but it’s also a powerful way to build trust within our teams. Being humble and honest about our knowledge gaps encourages others to do the same. It creates an atmosphere where asking questions and seeking help are seen as strengths rather than weaknesses.

Shift in Mindset: It’s a real game-changer to shift our mindset away from always needing to appear competent. Instead, focusing on showing our ability to learn and adapt makes us more resilient. It takes away the fear of failing and makes us more willing to take on challenges. Plus, it encourages us to keep improving, which is crucial in such a fast-paced field.

Craftsmanship Analogy: Comparing software development to craftsmanship really speaks to me. Like a craftsman honing their skills, we need to keep expanding our knowledge base. It’s not about being an expert right away, but about constantly growing and learning. Expertise comes with time and practice.

Personal Experience: I’ve seen firsthand how embracing ignorance has helped me grow both personally and professionally. Being open about what I don’t know and actively seeking to learn has boosted my confidence and strengthened my relationships at work. Asking questions and being open to feedback have been key in my development as a developer.

Conclusion: “Expose Your Ignorance” is a great reminder that it’s okay not to have all the answers. By embracing our ignorance and actively seeking knowledge, we create opportunities for growth and improvement. It’s a lesson I’ll definitely keep in mind as I continue on my career path.

From the blog CS@Worcester – CS: Start to Finish by mrjfatal and used with permission of the author. All other rights reserved by the author.

Object-oriented testing is about ensuring that the object-oriented software works as it should. Testing is important in any project so it’s important to make sure that the code isn’t buggy or crashes. Object-oriented testing has specific techniques that test our object-oriented program by focusing on what matters the most like unit, subsystem, and system testing. Unit testing breaks down each class and test, it helps catch errors and figure out if things are talking to each other as they should be. Subsystem testing is like testing a group of classes together, it shows how they work as a team and how it handles anything outside of it. System testing makes sure to test everything in the system and check if everything works together, leaving a top-notch system. When it comes to object-oriented testing it’s important to look out for bugs, test everything, and clean code sooner rather than later. Testing is about finding any bugs and errors that can be fixed, so looking for those bugs would help solve any problems the software has.  Instead of cleaning up the code after it’s completed, it’s useful to test it early and keep at it so that the issues are fixed sooner rather than later.

I felt like object-oriented testing has given me more information about testing and even though I know that there are different types of testing it’s given me a better idea of how they work. This reading is important for software developers who would rush through their work and not practice clean code because it emphasizes testing earlier to find any errors and clean up code that could make it confusing for other software developers. The reason why I chose this topic is because object-oriented testing is important in software development and without this information, it can cause a lot of problems and more work for developers to do when it’s avoidable and fixable. Object-oriented testing plays a huge role in software development making it important to know how it works and what is easy to detect. It also teaches software developers more about clean code and makes sure that the code is readable and understandable for other developers.

https://goodpractices.home.blog/2019/11/27/object-oriented-testing/

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

Going into my future projects, especially those for my summer fellowship, I have taken a distinct interest in Python. Throughout this course I have learned a great deal of how to test for object oriented compiler languages, using Java as our learning tool. I have wondered how testing might differ across different languages. And with a large project in Python coming up, I’d like to prioritize doing this the right way. I would like to practice in python testing to prepare. Luckily, recently posted last week, there was an article on the home page of Stickyminds surrounding python testing. The article’s title is Ensuring Reliable Cloud Applications: A Guide to Testing State Machines with Python by Konstantin Sakhchinskiy

Testing state machines in cloud applications involves heavy use of APIs. Python scripting is effective to help to ensure reliability, performance, and resilience. This is one way that an efficient library-heavy scripting language like Python would be useful for cloud applications. It is very effective at being implemented for many applications now utilizing the language itself, if only as an effective testing language. In the case of a cloud app for example, managing external browser profiles for automation and web scraping, users interact via API to execute their needed functions. Python can be used to expedite this process, as a way to manage state machines necessary to operate this cloud app. General approaches to testing on these state machines include separating output generation from state transition logic, focusing on individual states and transitions, testing each possible state transition, exploring boundary conditions, testing resilience to invalid input or disruptions, and checking system interaction within the broader context. The article also mentions some key tactics for mastering state machine testing, including testing for race conditions, assessing external disruptions and network challenges, handling heavy loads, and understanding technical details such as hidden states. Python scripting can automate testing processes. Using asyncio and httpx, scripts can simulate user interactions with the cloud service, testing responsiveness. Tailoring tests to specific needs and monitoring system performance using tools like Grafana and Kibana are essentia, and made more efficient with Python’s wide variety of usable systems. Overall, testing state machines is crucial for ensuring application reliability, and Python scripting offers effective ways to automate and simulate real-world scenarios for comprehensive testing.

My view on testing applications was expanded greatly by this article. The prevalence of automated testing libraries throughout other languages to enforce scripting was a surprise to me. And this article has helped me to understand the real role of specific and intentional testing of software. I was also surprised to find that state machines were very important to understanding how to test and break down code, as this is something I learned a lot about in the fall. I am not working with external applications or languages outside of Python for this project, so I will not utilize these libraries to automate my testing, but going forward this knowledge is imperative.

Source:

https://www.stickyminds.com/article/ensuring-reliable-cloud-applications-guide-testing-state-machines-python

From the blog CS@Worcester – WSU CS Blog: Ben Gelineau by Ben Gelineau and used with permission of the author. All other rights reserved by the author.

Performance Testing is a subject I just recently discovered is included in the Software Quality Testing umbrella term. Outside of casually monitoring CPU and GPU usage while using demanding software, I have little knowledge of what Performance Testing is. As a future worker in the Computer Science field, knowing how to report and minimize hardware stress must be an invaluable skill.

During my search to understand what Performance Testing is, I found an article written by Tricentis that included an in-depth explanation for each step (or deviation) of Performance Testing. Before going into detail, Tricentis highlights that performance testing is a direct solution to ensuring the stability and scalability of software. If performance testing is ignored, then users may encounter issues with speed, crashes, or data loss within their application. Testing seems to flawlessly integrate with CI/CD models, as testing should occur whenever a working model is available. This allows the development team to fix issues early before they sink too deep into the product’s foundation. Within the realm of performance testing, some subtests aim to find results for specific metrics. A few that I was unfamiliar with are as follows. Regression tests seek to test two states of the program, one being the current model, and the other being one with experimental changes. Both of these models work together to compare the hardware performance. These are used to highlight whether changes from the most recent version are more damaging to the product. Functional tests focus more on the software aspect of the product. Using this test will yield results on whether the UI elements, APIs, etc are all functioning correctly. Lastly, load testing seeks to put the product under great stress by simulating several concurrent users. Testing with this will give the developers insight as to how their product will function once published to the general public. Metris from load testing will help reinforce the product before it can fail after its initial release.

Performance testing will yield several useful metrics to guide the development of the product. These metrics include hardware bottlenecks, response time, and load time. Issues about these metrics must be addressed during the development process as they directly impact the reliability of their product post-release. Untested hardware is an active detriment to the developers, and will further damage the development process. Following the idea of testing software ‘frequently and consistently’, developers must also view how the current state of their product affects the hardware it will operate on.

-AG

Source: https://www.tricentis.com/learn/performance-testing

From the blog CS@Worcester – Computer Science Progression by ageorge4756 and used with permission of the author. All other rights reserved by the author.

JavaScript is a programming language completely foreign to me. At one point in the near future, I see myself learning the basics just to gain general exposure to it. It differs enough from my first object-oriented language, that being Java, to the point where it seems valuable to have some experience in it. With this in mind, I began my search to see what testing framework would work for this language and how tests are conducted differently.

In my search, I discovered this testing framework named Jest. This is a testing framework that is most known for testing Javascript but has also seen usage with other languages such as Angular and React. According to the author of the blog post The Pros and Cons of Different Test Tools – Jest, Craig Risi, he immediately states this testing framework is much more user-friendly than others. Jest does not require the users to install packages or other external dependencies, as the framework has them already included. Furthermore, the included dependencies are often built on frameworks of other languages, such as Mocha or Chai, which makes Jest an incredibly flexible testing framework. An important inclusion Risi made to his blog post was the inclusion of all features Jest offers out of the box. These range from topics I’m knowledgeable of, such as Mocking and Unit Testing, and some that I’m new to, such as Snapshot Testing and Asynchronous Testing.

Towards the end of Risi’s blog post, he mentions the direct pros and cons of using Jest. As previously mentioned the versatility between languages, easy start-up, and abundant features are a testament to its practicality. Unfortunately, no testing framework is flawless. Several of these issues originate from the sheer size of Jest. Downloading this framework and testing smaller projects offer an issue of scalability. There are countless smaller frameworks, in terms of digital size and complexity, that are better suited for testing software. Additionally, all the features Jest includes come with their own learning curve. Users may never utilize some features as the scope of their testing does not call for Jest’s extensive collection. With both the pros and cons in mind, Jest is incredible in what it can achieve but development teams must consider the scale of their project to determine whether using it is appropriate. Reflecting on my experience with testing frameworks, I can understand the value of having that much utility from one framework, yet I don’t see myself creating a project ‘so big’ that would call for it’s entire library of features.

-AG

Source: https://www.linkedin.com/pulse/pros-cons-different-test-tools-jest-craig-risi

From the blog CS@Worcester – Computer Science Progression by ageorge4756 and used with permission of the author. All other rights reserved by the author.