During week 12, we learned about test driven development. Test driven development is a testing method that outlines what functions need to be tested, writes the tests, then writes the code. It is an iterative process that goes through each test one at a time, building on what is already written. 

In class, we practiced the method by building a Mars Rover. We read what the rover should do and made a list of what we needed to test and build. We started with the easiest thing on the list and wrote a test for it. Then, we wrote the minimum amount of code to make the test run. We tweaked the test block, if it needed to be, and then ran the single test. Next, we repeated the process for the next easiest thing on the list. We will repeat the process until all of the tests are written. 

Test driven development focuses on just the tests before the actual code is written. It was a little confusing at first to think about how to write a test before the code, but once it clicked, it was easy. 

I found a website on test driven development and it described the process in a slightly different way. BrowserStack explains test driven development as a “red-green-refactor” cycle. The red phase is when just the test is written and it doesn’t pass because there’s no code. The green phase is when the bare minimum code is written for the test to run and it passes. The refactor phase is tweaking the tests and code so everything runs and passes. The cycle is run through every time a new test is written and repeats until all of the tests are written and pass. 

Test driven development is a very useful method for debugging. Since the tests are being run at every new addition, bugs are found as they appear and do not slip into the cracks as easily. The method also is very efficient and allows the programmer to easily add new functions later on in development. The iterative process is easy to maintain for the programmer, and provides a much larger testing scope than other methods. 

I liked working with test driven development. I think the process is very organized and straightforward. I would definitely use this in the future for projects that require thorough tests and with functions that build on each other. 

Source referenced: https://www.browserstack.com/guide/what-is-test-driven-development

From the blog ALIDA NORDQUIST by alidanordquist 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.

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.

This week we learned about boundary value testing and equivalence class testing. Boundary value testing focuses on making sure the values in, out, and around the expected boundary works as it should. Equivalence class testing does the same, but also tests the function itself.

I wanted to know more about the two methods and found a blog post that explains them a little more in depth. The author, Apoorva Ram, says they are more thought processes than testing methods really. The thought process of boundary value testing is self explanatory: testing the edge boundaries of the function. The thought process of equivalence class testing is organizing every possible input into groups of expected outputs and testing the result from each.

Ram also explains the benefits of the methods and how they can be used in software testing. The two seem to go hand in hand. Planning your tests before writing them and knowing the expected output makes the testing process a lot smoother. You know what points you need to hit and have a plan to execute them. Additionally, knowing all the points you need to hit allows you to prioritize ones that are more important.

For example, say you have a boolean function that looks for the input value to be between 15 and 30, but accepts values from 0 to 100. Boundary testing would test the values of xmin-, xmin, xmin+, xnom, xmax-, xmax, and xmax+. In this case: -1, 0, 1, 50, 99, 100, and 101. It mostly makes sure the 0 and 100 boundaries work. But equivalence class testing breaks down the function into classes of values that will give every result: invalid inputs (under 0 and above 100), false cases (between 0-14 and between 31-100), and the true case (between 15-30). In this case: -1, 12, 20, 45, and 101. This method tests the valid ranges as well as the function ranges.

In my opinion, equivalence class testing is better than boundary value testing because it actually tests the function and not just the illegal argument exception, and it eliminates redundant tests like xmin+, xnom, and xmax-, all testing for the same output without actually testing the function. Though ideally, a mix of both would probably be the method I choose. For this example, I would test each equivalence class and its boundaries: xmin-, xmin, xmin+, xtruemin-, xtruemin, xtruemin+, xtruemax-, xtruemax, xtruemax+, xmax-, xmax, and xmax+ (-1, 0, 1, 14, 15, 16, 29, 30, 31, 99, 100, 101).

Blog post referenced: https://testsigma.com/blog/boundary-value-analysis-and-equivalence-class-partitioning/

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