Levels of Testing

Levels of testing refers to the different stages of testing software during the development cycle. Each level of testing aims to test specific aspects of the functionality of the software. The most common types of testing levels are unit, integration, system, and acceptance testing. Unit tests focuses on individual components, or units, of the system such as functions. Integration testing is used to ensure that these units work together as intended. System tests verify that the entire system meets the requirements specified by the customer. Acceptance testing is similar to system testing in that the whole system is verified. However while system testing tests the entire system, acceptance testing tests the final system.

Unit Testing

Unit testing is conducted at the code level, where each individual component is tested and the results are analyzed. Rather than manually testing each component, automating unit tests is highly recommended to reduce errors in analyzing and for increased efficiency. When making a unit test, an outline stating the expected results of the code should be made.

An example of an outline for a unit test for a calculator app that simply adds to numbers together would look like:

• Program should accept two numbers
• Program should return the sum of the two given numbers
• Program should handle negative values appropriately

Once the outline for the unit tests are made, then the actual code for the unit test should be written. The test will then check the code to verify it is doing what is expected once the test has run.

Integration Testing

Integration testing tests groups of individual components that are integrated into a system. This type of testing helps to identify any issue that may occur from coding errors or errors from the integration between units.

System Testing

System testing is performed on the application as a whole. All components are assessed against specific requirements made by the customer.

Acceptance Testing

Acceptance testing involves testing the final system. This includes the system’s functional and non-functional aspects such as performance, security, usability, and more. In this testing level, end users are given access to the software to ensure the end users’ needs are being met.

Conclusion

This resource was chosen because it clearly described the different testing levels, their purpose, and why each level is important. I enjoyed reading this article because it was written in a way that made understanding easy. In the future, when creating a complex system, I will be sure to follow the different stages of testing at the appropriate time in the development cycle to prevent issues later in development.

Resources:

https://testsigma.com/blog/levels-of-testing

From the blog CS@Worcester – Zack's CS Blog by ztram1 and used with permission of the author. All other rights reserved by the author.

In my quest to deepen my understanding of effective software development methodologies, I delved into the principles of Test-Driven Development (TDD) through the article “Why Test-Driven Development Works” on InfoQ. This article serves as a comprehensive guide to the fundamentals and benefits of TDD, articulated through the lens of seasoned developers.

Why This Article?

I chose this article because of its practical approach and real-world relevance. InfoQ is known for its high-quality content aimed at software professionals, making it a reliable source for learning about cutting-edge practices like TDD. The article provides a mix of expert opinions and case studies, which helps bridge the gap between theory and practice—a crucial aspect for students and new developers.

Insights and Reflections

The article outlines the core process of TDD which involves writing tests prior to code, ensuring that software development is led by requirements that are continuously tested against. This process is detailed through the cycle commonly known as red-green-refactor:

1. Red: Write a test that fails.
2. Green: Write code that makes the test pass.
3. Refactor: Improve the existing code with confidence.

What resonated with me was the emphasis on how TDD enhances code quality and robustness. By focusing on requirements before implementation, TDD helps prevent future defects and reduces the time spent on debugging and maintenance. Furthermore, the article discusses the psychological benefits of TDD—providing developers with immediate feedback and a sense of accomplishment, which can boost morale and productivity.

Reflecting on these aspects, I recognize the value of TDD not just in preventing bugs but in fostering a disciplined approach to software development. It instills a mindset of accountability and precision, which are invaluable traits for any developer.

Application to Future Practices

Adopting the principles discussed in the article, I plan to incorporate TDD into my academic projects and future professional work. Embracing the red-green-refactor cycle will be crucial, especially in collaborative environments where code quality and consistency are paramount. By practicing TDD, I expect to enhance not only the technical quality of my projects but also the collaborative dynamics within development teams.

Conclusion

The InfoQ article “Why Test-Driven Development Works” is an essential resource for anyone looking to implement TDD effectively. It offers a blend of theoretical underpinnings and practical advice, making it particularly useful for students and new developers. The insights gained from this article will undoubtedly influence my approach to software development, guiding me towards more structured, efficient, and reliable coding practices.

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.

I’ve been working on a homework assignment where I’ve been tasked with undergoing the process of test-driven development while working on a code kata where I am to count the number of times each word in a string appears whilst ignoring special characters, spaces and new lines.

To summarize what test-driven development entails quickly, I consulted a blog post by Denis Peganov. Basically, when you do test-driven development, you write tests before you write the code. In practice, this means you have to figure out what the inputs and outputs should be for the code you’re working on, then organize the order with which you should be fulfilling tests in. Denis also mentions the cycle of test-driven development with the red, green and refactor phases. You start with a failing test in the red phase, write the minimum code to make the test pass in the green phase, then, as the name implies, refactor the program to enhance the program’s design if necessary. Denis goes further to make a great point that the iterative nature of test-driven development lends itself to modularity and can create a flow to the evolution of the codebase where this strategy is being applied.

I have to say, doing this in practice is fairly enjoyable up until the point where you have to rewrite everything. The flaw (while it is based upon the practitioner of this strategy of software development) is that if you ‘mess up’ the order of the tests, you can reach a point at which your design needs to change so drastically that you are effectively working backwards. This doesn’t necessarily mean that the strategy is bad, it’s moreso that it does require practice and experience in order to have a smooth development. It also requires the willpower to refactor massive chunks of code when you reach a critical refactor point, which for me it may just be that I’m rusty with regard to coding.

Regardless of this critique, test-driven development does seem to be the best of both worlds. Not only are you able to properly plan software from specifications, but you also get to actively see the results of your code while developing it. It seems incredibly efficient in comparison to other philosophies of software testing, and for me it’s honestly more engaging to write tests and write the code than just writing tests and reading the code that already exists or the specifications alone.

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

A little while ago we learned how Define-Use Testing works as well as how to use it ourselves. However at the time, it still confused me a bit and I wasn’t really sure what it was used for. After reviewing the previous work we’ve done and coming back across this topic, I realized that I still didn’t fully understand what was going on. I decided to look further into it and to do some research in order to understand it more. As a result, I found a website called testsigma which I believe does a really good job at explaining what Define-Use case testing is, its advantages and its disadvantages, and when to use it.

With the website’s help, I’m going to explain everything that you need to know about Define-Use case testing in order to understand it and to know when to use it. As for what a use-case itself is, the website states “The concept of a Use Case can be likened to a real-life scenario in which users and their objectives are present. A Use Case outlines the events unfolding when these users interact with the system to achieve their objectives. The Use Case allows for a thorough understanding of the overall behavior and functionality of the system from the user’s point of view.” Essentially this means that a Use Case shows all of the decisions that were made while the user was trying to achieve their outcome, allowing you to understand all of the behavior and functionality from the user’s perspective. Use Case Testing was designed to make sure that the system that is currently being tested meets all of the expectations. It essentially simulates different real-life scenarios in order to make sure the software does everything that it is supposed to. While the website gives a lot of different advantages and disadvantages of Use Case testing, I’m just going to list a few. Some advantages include helping to identify and validate the functional requirements of a system or software, explaining how the software will be used in real-world scenarios, and helping uncover potential defects. Some disadvantages include it could be time-consuming and require a lot of effort, it may not cover all scenarios which could leave to gaps in test coverage, and it relies on the accuracy of the use case documentation. As for when to use Use Case testing, the website gives a checklist which I found to be extremely useful in my understanding of when to use it. I’m really glad I found this website as it further helped my understanding of the topic, and it has helped me prepare for my final exam. The checklist states:

“1. Review the software requirements, specifications, design documentation, and use case scenarios. 

2. Identify the possible scenarios related to the use case. 

3. Determine the functional requirements of the use case. 

4. Create test cases for each possible use case scenario. 

5. Define the expected results of the use case scenarios. 

6. Execute the test cases and compare them with the expected results. 

7. Retest the fixes after identifying failed test cases. 

8. Check for exceptions and errors for each use case scenario. 

9. Verify compliance and security requirements for each use case scenario. 

10. Monitor the system’s overall functionality across use scenarios. 

11. Validate the overall system performance with user expectations”

From the blog CS@Worcester – One pixel at a time by gizmo10203 and used with permission of the author. All other rights reserved by the author.

For this week’s blog post, I decided to discuss the article “A Practical Introduction to Test Driven Development” by Luca Piccinelli. I chose this article because it compliments the test driven development topic in the syllabus. This article discusses what test driven development is, the costs of using test driven development versus not using it, and gives some examples of how test driven development is done.

The author of this article is very blunt about their feelings towards test driven development. “These days you read a ton of articles about all the advantages of doing Test Driven Development (TDD). And you probably hear a lot of talks at tech conferences that tell you to “Do the tests!”, and how cool it is to do them. And you know what? Unfortunately, they are right (not necessarily about the “cool” part, but about the useful part). Tests are a MUST!” As mentioned by the other, some of the advantages that come with using test driven development are you write better software, you have more protection from breaking the whole project when new features are introduced, your software is self documented and you avoid over engineering. However, there is a glaring issue with using test driven development: the cost.

The author discusses the costs of using test driven development versus not using test driven development in good detail. The main differences in the costs are not in terms of value but in timing. “It [test driven development] costs a lot! Probably someone is thinking “but it costs even more if you don’t do the tests” — and this is right, too. But these two costs come at different times: you do TDD you have a cost now. You don’t do TDD you will have a cost in the future.” The author feels the best way to reconcile this problem is to do what comes naturally for the project’s development. “The most effective way to get something done is doing it as naturally as possible. The nature of people is to be lazy (here software developers are the best performers) and greedy, so you have to find your way of reducing the costs now. It’s easy to say, but so hard to do!” The next section I will be covering discusses how the author feels test driven development should ne implemented.

The author feels that the best way to implement test driven development practices would be to use a top down approach. “[A top down] approach is the best friend of over-engineering. You typically build the system in order to prevent changes that you think will come in the future, without knowing if they actually will. Then when some requirement changes, it typically happens in a way that doesn’t fit your structure, no matter how good it is. For me the key to drastically reducing the immediate cost of writing with TDD has been to take a top-down approach”

Article: https://www.freecodecamp.org/news/practical-tdd-test-driven-development-84a32044ed0b/

From the blog CS@Worcester – P. McManus Worcester State CS Blog by patrickmcmanus1 and used with permission of the author. All other rights reserved by the author.

For this week’s blog I decided to dive a bit deeper into writing testable code because I think it is a valuable skill. While searching for blogs that discuss this topic I found “TDD: Writing Testable Code” by Eric Elliott. In this blog, Elliott explains the importance of writing testable code barriers to writing testable code, and different tips to keep in mind to write testable code. 

The first thing he touches on is how a good quality process is essential to continuous delivery. One of the most important principles, he mentions, is the separation of concerns because “simplifies complexity and enhances the overall quality of your work”.

In the overview about testable code, he explains that achieving testable “allows for more efficient and effective identification of defects, ensuring higher quality and reliability”. The key characteristics of testable code are modularity (when code is organized into discrete units), clarity (code that is easily comprehensible), and independence (when code can be tested in isolation).One of the barriers of writing testable code that Elliott describes is tight coupling in code. The coupling in code is the instance where changing one part of the code can heavily impact or even break the functionality of another part in the code. If one part is changed there can be a chain reaction of bugs in various other parts of the code. Right coupling can be caused by parent class dependencies, shared mutable states, concrete class dependencies, event chains, state shape dependencies, control dependencies or temporal coupling. He explains that to write testable code you must reduce the forms of tight coupling mentioned because it makes testing easier and promotes a “more sustainable and scalable codebase”. This is why testable code and maintainable code are the same thing. 

Later he explains the test first vs test after approaches. The test first approach is the test driven development approach where tests are written before the code. The benefits include better developer experience and clearer requirements and design. The test after approach is where the tests are written after the implementation which leads to a probability of biased tests and reduced code coverage.

One of the last sections provided is about the separation of concerns which a  design principle for programs into distinct sections that  addresses separate concerns”. This principle is important because it allows independent testing and allows developers to isolate problems more effectively in addition to allowing the ability to make changes without unintended consequences. He describes that he likes to isolate into 3 distinct sections business and state logic (the core logic of the application), user interface, and I/O and effects (the part of the application that interacts with the database).

I think this source was well organized and easy to follow. It made the text easy to understand and provided great advice for writing testable code. I think the last section about how he likes to isolate the sections was especially helpful and I will keep it in mind for the future.

From the blog CS@Worcester – Live Laugh Code by Shamarah Ramirez and used with permission of the author. All other rights reserved by the author.