Boundary testing is an integral aspect of testing software used to test the extreme limits of a program. It consists of testing the minimum value, right above minimum, a nominal value, a maximum value, and right below maximum. If we’re using a strong testing method, we would also test below the minimum and above the maximum.

By testing this way, we can assure that the “boundaries” of the program work correctly as well as center of those boundaries. We can also ensure that invalid inputs return an invalid response from the program.

As Jayesh Karkar says in his blog “Everything You Need to Know About Boundary Value Testing”, “This testing is imperative when it comes to testing the functional competence of software modules.”

He mentions some factors that justify the necessity of boundary value testing. These include that functionality errors are likely to occur more often at boundaries, it only tests a small range of values making it simple to execute, and due to the testing being based on guidelines and framework there is no chance of compromising the boundary values which ensures maximum test effectiveness.

Now as most of us in the computer science field know, there are many ways of testing programs. Boundary value testing is just one of them. With this in mind, boundary value testing is certainly one you should keep in your “tool kit” and combine with other forms to create the most effective tests possible.

As I continue my career as a student and my future career as a computer scientist, I plan on taking this form of testing with me wherever I go as a fundamental.

From the blog CS@Worcester – DPCS Blog by Daniel Parker and used with permission of the author. All other rights reserved by the author.

URL: TestoMat Mocha and Chai Web Application Testing
The chosen article walks us through the implementation and inner workings of two JavaScript testing tools. Mocha and Chai are two different testing tools that, when combined, form a powerful addition to any software development project. Mocha serves as a test runner, being responsible for executing the test suites. While Mocha can function independently and does not necessarily require any additional tools, combining it with Chai, an assertion library, allows for the validation of expected outcomes in test cases.

The blog post also provides us with several reasons why the utilization of both tools can help improve your software development speed and quality. Among the listed benefits are:

Mocha:

• Flexible and customizable testing.
• Simplifies the testing of asynchronous code.
• The ability to apply before, beforeEach, after, and afterEach.
• Runs tests in both browser and Node.js environments.

Chai:

• Provides clear and expressive assertions that improve the readability of your test scripts.
• Allows you to write descriptive tests using natural language constructions.
• Enables the creation of custom assertions tailored to your specific testing requirements.
• Supports assertions on complex data structures such as arrays, objects, etc.

At the end of the article, it presents an opportunity to implement Mocha with some real-world examples, which is great and especially helpful for anyone unfamiliar with the framework.

The reason I chose this article relates mainly to another class. I will be taking on the task of developing test suites for the Thea’s Pantry app. I also found it interesting because it explains why you would use both Mocha and Chai instead of using other tools or Mocha alone.

The content is helpful and does not seem to include any bias by presenting Mocha and Chai as the only testing environment for JavaScript. Instead, it highlights their pros and invites the reader to determine whether these tools will suit their specific use case. One thing that caught my attention about using Mocha is its ability to generate documentation through testing. This is great, as it will help me better understand how documentation related to testing is created and how useful it can be.

Mocha and Chai’s use of natural, human-like language makes testing much easier. The use of natural or human-like language in programming often makes me skeptical of such tools, libraries, or frameworks. Sometimes, this characteristic is marketed as a way to draw people in, as if it will help them code more effectively. Although Mocha and Chai are distinct tools, their implementation of this characteristic leans more toward improving readability for the programmer rather than for just anyone. What I mean by this is that they avoid technical or overly formal wording in favor of keywords that resemble everyday human language.

From the blog CS@Worcester – CS Today by Guilherme Salazar Almeida Nazareth and used with permission of the author. All other rights reserved by the author.

This week in class, we took another deep dive into the world of test doubles. This week pertained to mocks specifically. While doing class activities related to mocks this week in class, it seemed to me that mocks are easier to use than stubs while offering more insight on how a program is supposed to run. With that in my mind, I took the opportunity to further learn about mocks. I ended up finding a blog that gave me an even better understanding about mocks, and it is called “Understanding Mock Objects in Software Testing: A Tale of Simulated Reality”.

One quote from the blog does a great job of summing up what mocks are. “It’s like a stand-in actor in a movie – the mock object behaves like the real one but is controlled within the testing environment.” Mocks mimic the actions of the object it replaces when called upon in a test class. Mocks check the order and frequency of method calls in which they are used, which stubs don’t do.

Frameworks

There are mocking frameworks for many different languages. Some common mock frameworks include Mockito in Java, Moq in .NET, and Jasmine in JavaScript. Mockito is popular for its simplicity. It does well to create mock objects for a certain class or database that simulates how a real object would respond in the program. Moq is primarily used for C# applications and is popular for its strong typing and fluent interface. A language with strong typing demands specification over data types, so variables must have a type when they are defined. Jasmine mocks HTTP requests, which allows front-end applications to be tested without back-end interactions.

Pros

Some of the benefits of using mocks is that they are used in a controlled and predictable environment, and they are efficient. Mocks give predetermined behaviors and don’t leave the testing framework, which leads to consistency and predictability. And since mocks are used in the place of real-world objects, the testing process is sped up. It allows testers to isolate external factors and focus solely on the code being tested. Mock testing is also a cheaper alternative to testing real-world objects that may be very expensive.

Cons

A drawback of mock testing is that while tests pass in the testing, they may not in real-world situations. And just like stubs, changes to the system can cause mocks to become outdated, so it is important that mocks are frequently provisioned in order kept up to date with any changes to the system being tested.

Example

In the example above Mockito is the framework being used. WeatherService represents the external dependency, and WeatherModule represents what is being tested. mockService should return “Sunny” when “New York” is the string in the .getWeather() method. This allows WeatherModule to be tested in isolation. module.getWeather() is then assigned to the string variable “weather”. From there, weather is tested for if it returns “Sunny”. This is fascinating because the tests compile and are ran using make-believe objects

Reference

https://www.thetesttribe.com/blog/mock-testing/

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

During week 10, we experimented with stubs and mocks. Stubs and mocks are two forms of testing doubles that allow a team to write tests before the whole program has been written. Stubs and mocks simulate the code’s fleshed out methods and allow for testing methods to be written in advance. 

Stubs are methods or classes that are either empty or return a set value so they can run and be tested. Stubs are state testers and focus on testing the outcome of methods. Mocks are more dynamic, the test block can define what it wants the outcome of any method to be and then test for that outcome. It can also test for multiple set outcomes in the same block. Mocks are behavior testers and test the interactions between methods, rather than the outcome. 

The reference I found was a blog that compares and contrasts stubs and mocks by BairesDev to get a better idea of their use cases. They explained what stubs and mocks are, the advantages and disadvantages of each, and the situations to use both of them.

Advantages for stubs are their predictability and isolating the method. They will always return what you are expecting because of how simple they are. Since stubs do not involve any other calls or methods, they are great at isolating testing to just that method. Disadvantages are user error and the lack of behavior testing. The user might have a discrepancy in what they return in the stub and what they expect in the test. If your method needs to interact with other methods, stubs are not great for testing behavior because they only look at the outcome. 

Advantages for mocks are being better at testing subtle bugs and issues and testing the interactions between methods in your code. Since mocks are behavioral tests, if the methods don’t interact how they’re expected to, the test will not pass, which goes beyond what stubs do. Disadvantages are increased complexity and brittle tests. Mocks make your tests more complex than if you were testing the fully written code, which may take more time to adapt to once the program is finished. Brittle tests can occur if tests are too connected to the mock expectations, and small changes can cause a lot of errors. 

Overall, stubs are useful when testing independent methods and those that only need to be tested for the outcome. Mocks are useful when methods are dependent on others and can find errors that might not show up if you were just testing outcomes. Both are great when writing tests, but have different applications and both should be used when testing programs. 

Source: https://www.bairesdev.com/blog/stub-vs-mock/

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

Decision table testing is a software testing  technique  that connects  inputs to  outputs. It  utilizes a structured table  for all possible test cases to be analyzed. A  condition represents  a variable or input that  impacts a process, and the action is the outcome influenced by a condition. Condition alternatives define all  possible values for a condition. Each row in  decision table testing is a connection between a  condition and action, with values typically  represented as true or false or yes or no. For instance testing a login system, the conditions are username and password. Action is a successful or  failed  login attempt. Switch table is classified as a type of decision table testing where  a single condition decides a design. A  traffic light system is an instance of decision table testing, with traffic light color being the condition. Action is cars  stopping or  continuing through traffic depending on traffic light color. In  rule based decision table, columns are actions and conditions, and the  row indicates if a condition and action  for a column match the condition and action for a rule.  Another type of decision based testing is a limited decision table. Limited decision tables have a simple and independent condition with an example of a login system. A  login attempt result  is known from a  username and password being correctly entered. Username and password are independent  conditions, as a system identifies username or password is correct, regardless of each other.

First step in creating a decision table is identifying the conditions. A login system will be used for an example. Conditions are valid   username or  password. Once the conditions are constructed, the next step is defining  possible condition alternatives. In a class activity, the conditions were gpa and credits. A condition with either  gpa or credits is allowed  multiple  values  such as gpa > 2.5  or gpa < 4.0. Once condition and condition alternatives have been created, the actions have to  be defined. The next step is setting up  rules for the decision table. After the rules have been set. The table should hold conditions, actions, rules, and condition alternatives. Table should be now filled. The last step to creating a decision table is identifying and deleting  repetitive rules. Prior to taking a software testing class, I had little knowledge on decision table testing. I choose this blog as a chance to expand my  understanding of decision testing. After reading the article, I have a better understanding of decision testing compared to my first learning decision testing.

article: https://www.browserstack.com/guide/decision-table

From the blog CS@Worcester – jonathan&#039;s computer journey by Jonathan Mujjumbi and used with permission of the author. All other rights reserved by the author.

Software testing is a significant part of confirming the functionality, reliability, and performance of software products. Out of all the diverse types of tests, Basis Path Testing is one essential technique for confirming the control flow of a program. In this blog, we share the concept of Basis Path Testing, its importance, and how it is applied in software testing.

What is Basis Path Testing?
Basis Path Testing is a white-box testing method that focuses on software control flow. It was formulated by Thomas J. McCabe as a part of the Cyclomatic Complexity metric, which calculates the amount of linearly independent paths in a program’s control flow. The approach is designed to test the software by executing all the independent paths through the program at least once to provide complete coverage of the code.

The goal of Basis Path Testing is to locate all the potential paths within a program and ensure that each of them is tested for possible issues. It helps in determining logical defects that are not obvious through other testing techniques, such as functional or integration testing.

Key Elements of Basis Path Testing
Control Flow Graph: The first step in Basis Path Testing is to design a control flow graph (CFG) for the program. This graph represents the control structure of the program, including decision points, loops, and function calls.

Cyclomatic Complexity: The second step is to compute the cyclomatic complexity of the program, which is the number of independent paths. The metric is calculated as:
V(G) = e – n + 2*P

Where, e is number of edges, n is number of vertices, P is number of connected components.
The cyclomatic complexity provides the minimum number of test cases required to exercise all the independent paths.

Independent Paths: After calculating the cyclomatic complexity, the independent paths in the control flow graph must be determined. These are paths that don’t reuse any other path’s sequence of execution.

Test Case Design: Once independent paths are identified, test cases are created to execute each path such that all aspects of the program’s logic are exercised.

Importance of Basis Path Testing
Basis Path Testing is particularly useful in revealing intricate logical errors that can result due to intricate control flow. By carrying out all independent paths, it ensures that nothing in the program is left untreated, and this reduces the chances of undiscovered defects.

The approach is used widely in unit testing and integration testing, especially for programs with intricate decision structures and loops. It is also a good approach to use in regression testing, where changing the codebase can probably introduce flaws into previously tested paths.

Conclusion
Basis Path Testing is a highly valuable method for thorough testing of software using independent paths through the control flow of a program. By understanding and applying this method, software developers are able to improve the quality of applications, reduce errors, and deliver improved software to end-users.

Personal Reflection
Having studied Basis Path Testing, I can see how this approach is essential to checking the strength of software systems. As a computer science major, what I have learned from my studies is that testing is not just about checking if the code runs but, more importantly, that the logic and correctness of running are checked. Basis Path Testing’s focus on cyclomatic complexity provides a clear, mathematical way to ensure that all possible execution paths are considered.

My experience is that application of this technique detects logical flaws in programs which would otherwise not be easily seen through normal debugging or functional testing. 

Citation:
“Basis Path Testing in Software Testing.” GeeksforGeeks, https://www.geeksforgeeks.org/basis-path-testing-in-software-testing/.

From the blog Maria Delia by Maria Delia and used with permission of the author. All other rights reserved by the author.

This week I am diving into an article by David Rodenas, PhD – a software engineer with a wide array of knowledge on the topic of Software Testing. I found the article, “Improve Your Testing #13: When F.I.R.S.T. Principles Aren’t Enough” which is one of many posts from Rodenas that helps provide insights to Software Testing that only a pro could provide. Through this article, Rodenas walks through each letter of the FIRST acronym with new meaning – not replacing what we know but instead enhancing what we know. As the author teaches us these new ways of understanding, examples are provided to look for ways in our own work in which these can be applied.

The acronym can be read as: Fast, Isolated, Repeatable, Self-Verifying, and Timely, but taking this one step further we can acknowledge the version that builds on top of this as: Focused, Integrated, Reliable, Significant, and Thoughtful. It is obvious that these definitions are not opposites of each other, they should also exist cohesively in our quest for trustworthy software.

One pro-tip that sticks out to me here is keeping your code focused by avoiding using the word “and” in the test name. When I first read it – it seemed kind of silly, but in a broad-sense it really does work. The writer relates this to the Single Responsibility Principle – by writing tests with a clear focus, our tests are fast and purposeful. Another takeaway is the importance of writing reliable and significant tests. Tests should not only validate but also provide meaningful information for what went wrong to cause them to fail. A test that passes all the time but fails to catch real-world issues is not significant. Similarly, flaky tests—ones that pass or fail inconsistently—break trust in the testing suite and should be avoided. Rodenas also emphasizes that integrating tests properly is just as important as isolating them. While unit tests should be isolated for precision, integration tests should ensure that components work together seamlessly. A good balance between both approaches strengthens the overall reliability of a software system.

Ultimately, this article challenges us to go beyond simply following FIRST principles and to think critically about our testing strategy. Are our tests truly adding value? Are they guiding us toward our goal of thoroughly tested software, or are they just passing checks in a pipeline? By embracing this enhanced approach to testing, we can ensure that our tests serve their true purpose: to build confidence in the software we deliver.

From the blog cameronbaron.wordpress.com by cameronbaron and used with permission of the author. All other rights reserved by the author.