This week in class we learned about path testing, which is a white box method that examines code to find all possible paths. Path testing uses control flow graphs in order to illustrate the different paths that could be executed in a program. In the graph, the nodes represent the lines of code, and the edges represent the order in which the code is executed. Path testing appealed  to me as a testing method because it gives visual representations of how the source code should execute given different inputs. I took a deeper dive into path testing after this week’s classes and found this blog that gave me a deeper understanding of path testing.

Steps

When you have decided that you want to perform path testing, you must create a control flow graph that matches up with the source code. For example, the split of direction between nodes should represent if-else statements and for while loops, the nodes towards the end of the program that have an edge pointed at an earlier node. 

Secondly, pick out a baseline path for the program. This is the path you define to be the original path of your program. After the baseline is created, continue generating paths representing all possible outcomes in the execution. 

How many Test Cases?

For a lengthy source code, the possible outcomes could seem endless and could therefore end up being a difficult, time-consuming task to do manually. Luckily, there is an equation that determines how many test cases a program will need with path testing.

C = E – N + 2P

Where C stands for cyclomatic complexity. The cyclomatic complexity is equivalent to the number of linearly independent paths, which in turn equals the number of required test cases. E represents the number of edges, N is the number of nodes, and P is the number of connected components. Note that for a single program or source of code, P = 1 always.

Benefits

Path testing reveals outcomes that otherwise may not have been known without examining the code. As stated before, it can be difficult for a tester to know all the possible outcomes in a class. Path testing provides a solution to that by using control flow charts, where the tester can examine the different paths. Path testing also ensures branch coverage. Developers don’t need to merge code with an existing repository because the developers can test in their own branch. Unnecessary and overlapping tests are another thing developers don’t have to worry about.

Drawbacks

Path testing can also be time consuming. Quicker testing methods do exist and take less time off further developing projects. Also in many cases, path testing may be unnecessary. Path testing is used often by many DevOps setups that require a certain amount of unit coverage before deploying to the next environment. Outside of this, it may be considered inefficient compared to another testing method.

Blog: https://blog.testlodge.com/basis-path-testing/

From the blog Blog del William by William Cordor and used with permission of the author. All other rights reserved by the author.

I chose this blog because I was interested in learning how to optimize the selection of test cases without sacrificing thorough coverage. During my search, I came across an article on TestGrid.io on equivalence partitioning testing, which was fantastic in removing redundancy from testing. As I develop my programming and software testing skills, I have found this method especially useful in making the testing process simpler.

Equivalence partitioning is a testing technique applied to partition input data into partitions or groups based on the assumption that values in each partition will behave similarly. Instead of testing each possible input, testers choose a sample value from each partition and hope the entire group will result in the same output. This reduces the number of test cases but still provides sufficient coverage.

For example, if a program is able to accept input values ranging from 1 to 100, equivalence partitioning allows the testers to categorize them into two sets: valid values (1-100) and invalid values (less than 1 or more than 100). Rather than testing every number in the valid set, a tester would choose sentinel values like 1, 50, and 100. Similarly, they would test the invalid range with 0 and 101. This is time-efficient but identifies errors simultaneously.

I employed the TestGrid.io article because it explains equivalence partitioning in an understandable and systematic manner. Much other testing material is too complex or ambiguous for newcomers, but this article simplifies it and incorporates real-world examples. This allowed it to be simple not only to understand the theory, but also to apply the method to real-life situations.The article also discusses the advantages of equivalence partitioning, including reducing redundant test cases, being more efficient, and offering complete coverage. As an individual interested in improving my testing methods, I found this useful because it corresponds with my goal of producing better, more efficient test cases without redundant repetition.

Equivalence partitioning testing is a sound approach to maximizing test case selection. It enables the tester to focus on representative cases rather than testing all possible inputs, which is time- and effort-efficient. The TestGrid.io article provided a clear understanding of how to implement this method and why it is significant. For me, learning effective test methods like equivalence partitioning will make me more efficient in my coding, debugging, and software developing abilities, prepared for internships, projects, and software engineering positions.

Blog: https://testgrid.io/blog/equivalence-partitioning-testing/

From the blog CS@Worcester – Matchaman10 by tam nguyen and used with permission of the author. All other rights reserved by the author.

This week, our class learned about path testing. It is a white box testing method (meaning we do not actually run the source code) that tests to make sure every specification is met and helps to create an outline for writing test cases. Each line of code is represented by a node and each progression point the compiler follows is represented by a directed edge. Test cases are written for each possible path the compiler can take.

For example, if we are testing the code block below, a path testing diagram can be created: also shown below. Each node represents a line of code and each directed edge represents where the compiler goes next, depending on the conditions. Test cases are written for each condition: running through the while loop and leaving when value is more than 5 or bypassing it if value starts as more than 5.

I wanted to learn more about path testing, so I did some research and found a blog that mentioned cyclomatic complexity. Cyclomatic complexity is a number that classifies how complex the code is based on how many nodes, edges, and conditionals you have. This number will relate to how many tests you need to run, but is not always the same number. The cyclomatic complexity of the example above would be (5-5)+2(1) = 2.

Cyclomatic Complexity = Edges – Nodes + 2(Number of Connected Components)

The blog also explores the advantages and disadvantages of path based testing. Some advantages are performing thorough testing of all paths, testing for errors in loops and branching points, and ensuring any potential bugs in pathing are found. Some disadvantages are failing to test input conditions or runtime compilations, a lot of tests need to be written to test every edge and path, and exponential growth in the number of test cases when code is more complex.

Another exercise we did in class was condensing nodes that do not branch. In the example above, node 2 and 3 can be condensed into one node. This is because there is no alternative path that can be taken between the nodes. If line 2 is run, line 3 is always run right after, no matter what number value is. Condensing nodes would be helpful in slightly more complex programs to make the diagram more readable. Though if you are working with a program with a couple hundred lines, this seems negligible.

When I am writing tests for a program in the future, I would probably choose a more time conscious method. Cyclomatic complexity is a good and useful metric to have, but basing test cases off of the path testing diagram does not seem practical for complex codes and tight time constraints.

Blog post referenced: https://www.testbytes.net/blog/path-coverage-testing/

From the blog CS@Worcester – ALIDA NORDQUIST by alidanordquist and used with permission of the author. All other rights reserved by the author.

The blog post that I chose to write about this week is one that gives an overview of equivalence class and boundary analysis testing. The main reason why you would use these is to reduce the number of tests you run for a program while still testing full functionality and not sacrificing coverage. It does this by sectioning the range of inputs into different equivalency classes. Equivalency classes are groups of inputs that in theory should behave identically when put into the tested function. The blog then shows a helpful diagram showcasing what this looks like plotted on a number line. This way, tests will give better information by only testing the function where problems may arise and will detail the behavior of the function near edge cases better than other methods.

The blog post also details how you can represent the classes as functions themselves for where the inputs would be, for example, true, false, or valid, by defining ranges of values with interval notation. After then going over boundary test cases, the author explains how these two methods can be used together to efficiently test around the limits of the function behavior. The blog concludes with another example plotted on a table that shows how equivalence classes and boundary testing can be combined to use a minimum number of tests while also ensuring that you test the function at its most important parts where the process will change based on inputs.

I selected this blog to help refresh myself for the upcoming test about different testing methods and to reinforce what I had learned in class. I think that one of the more important takeaways from this blog is the emphasis the author puts on combining the two methods not just because they are two different methods but because they strengthen the overall testing procedure, and this will make me think about how new testing methods can be combined to lead to better and more efficient test cases. Demonstrating the testing in terms of models on number lines and as graphs help visualize what is actually happening and why it works, similar to the models taught in class but the added element of real numbers with example values helps demonstrate the importance of this kind of testing and how it can be useful for any kind of real-world situation. As an introductory post to the topic, and in my case a review, it works well but from here I would like to look more into the different combinations of testing methods that can work well together and some that may not as I learn more methods through the rest of the class.

https://www.testbench.com/blog/equivalence-class-partioning-and-limit-value-analysis/

From the blog CS@Worcester – Computer Science Blog by dzona1 and used with permission of the author. All other rights reserved by the author.

REST (Representational State Transfer) APIs have become a cornerstone in modern software development, enabling seamless communication between different systems. For those new to the field, understanding REST APIs is essential as it forms the foundation for integrating various services and building scalable applications. The blog post “Rest API Tutorial — A Complete Beginner’s Guide” from Moesif provides an excellent introduction to this topic. Here, I will summarize the content of the blog, explain why I selected this resource, and reflect on what I learned from it.

Summary of the Blog Post

The Moesif blog post starts by explaining what an API (Application Programming Interface) is and introduces REST as an architectural style for designing networked applications. It highlights that RESTful APIs are stateless, meaning each request from a client to a server must contain all the information the server needs to fulfill it. The post further discusses key concepts such as HTTP methods (GET, POST, PUT, DELETE), status codes, and the importance of endpoints in API structure.

The guide provides practical examples to illustrate these concepts, making it easier for beginners to grasp. It also touches on best practices, such as the use of proper status codes to indicate request outcomes and keeping URLs clean and intuitive. The post ends by emphasizing the importance of good documentation and consistent API versioning to ensure ease of use and maintainability.

Why I Selected This Resource

I chose this particular blog post because of its comprehensive yet beginner-friendly approach. As a computer science student, I am currently learning about software architecture and development practices. This guide stood out for its clarity in explaining complex concepts and its practical examples, which help bridge the gap between theory and real-world application. I wanted to understand REST APIs better, not just from a theoretical standpoint but in a way that I could apply in future projects, making this resource ideal.

Reflections on the Content

Reading through the blog post was eye-opening. It clarified the purpose and usage of REST APIs and reinforced my understanding of HTTP methods and status codes. The emphasis on statelessness and how each request must be self-sufficient was particularly insightful, as I previously struggled with this concept. Additionally, I learned the significance of designing intuitive endpoints and properly using status codes to indicate different outcomes, such as 200 for success or 404 for not found.

This resource has given me the confidence to start building simple RESTful APIs. I now appreciate why good API documentation and versioning are critical — they help developers maintain and scale services effectively. Moving forward, I intend to apply this knowledge in my coursework and future software projects, ensuring that the APIs I develop are well-structured, easy to use, and maintainable.

Conclusion

Overall, the “Rest API Tutorial — A Complete Beginner’s Guide” was a valuable resource that provided me with a solid foundation in RESTful API development. I highly recommend it to any beginner looking to understand how APIs work and how to implement them in practical projects. For those interested, you can read the full post here.

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