Category Archives: CS-443

Learn How You Fail

Failing is unavoidable and it happens to everyone at some point. In reality, anyone who has never failed at anything has either stopped challenging themselves or learnt to ignore their own mistakes.

Your skills are progressing but you are still failing and have some remaining weaknesses. So instead of drowning in a sea of self-pity over previous failures, develop some self-awareness of your own patterns, habits, behaviors, and other factors that play a role in causing you to fail. When you are aware of yourself and the things that mess you up, you give yourself the option of either trying to solve the issues or cut your losses. You cannot succeed in all, and recognizing your shortcomings is critical because it allows you to actively recognize distractions and concentrate on your goals.

Accept that there will be certain stuff you aren’t very good at.

Once you learn to accept your failures you can set realistic limitations on your goals.

Often times I get irritated by the fact I keep failing, but if I accept it, my failures become learning opportunities. I can then seek out guidance and learn new skills from someone who knows more about code than I do. It is hard, honest work to admit your shortcomings and it will take a lot of failures, but I am confident that eventually, it will all be worth the frustration.

From the blog cs@worcester – Coding_Kitchen by jsimolaris and used with permission of the author. All other rights reserved by the author.

Software Testing: Jacoco

For this week’s blog post I chose to focus on a topic that I had to research for an honors project, this being Jacoco. In case you are not familiar with it, Jacoco is a software tool that is used to test code coverage, which in short just involves checking how much of a system’s code is being tested. This may sound redundant if you, like myself, have only really been using tests on small scale projects, but as a program’s complexity increases so does the complexity of the tests required. Additionally you will have to write more and more tests, making it difficult to determine exactly what has been covered. Before anything else however, I am going to break down exactly how code coverage is checked.

Having not known about Jacoco at all, I found this article on Baeldung that was extremely useful in understanding how it worked. As I stated the essential idea is to check code coverage, which consists of line and branch coverage. Line coverage essentially means Jacoco will check how many lines of code are being executed during tests to determine which are actually being tested. Branch coverages is similar to this, but focuses on checking the logical splits in the program, such as conditionals like if statements. The total amount of branches present is then compared to the amount that were covered to give the results. There is one more aspect of a program Jacoco checks for, being cyclomatic complexity. This is essentially a measure of the logical complexity of the program, or how many branches the program has. Now that we know what Jacoco is we can discuss why you might want to use it.

This testing tool certainly has some major benefits for anyone working on a large system. If you are working on writing tests for a system, it may begin as something straightforward, but as the system grows it will become increasingly hard to track exactly what components are being tested. This is where Jacoco comes in, allowing you to see exactly what lines, or just classes, are being tested so you can ensure there is sufficient test coverage. Additionally it can show you the degree of logical complexity present in the system, which could then be adjusted accordingly. One point to keep in mind that is mention in this article is that you should not get tunnel vision when trying to increase code coverage. High code coverage does not necessarily mean the tests being run are adequately testing the system. It is more important to focus on writing a few functional tests for each component rather than just having a plethora of tests that do not really do much of value. This article discusses Jacoco in more detail than I can so if this interests you I would recommend checking it out!

Source:

https://www.baeldung.com/jacoco

From the blog CS@Worcester – My Bizarre Coding Adventures by Michael Mendes and used with permission of the author. All other rights reserved by the author.

Code Review

Link: https://smartbear.com/learn/code-review/what-is-code-review/

According to the article linked above, to code review is to consciously and systematically convene with one’s fellow programmers to check each other’s code for mistakes. There are many different ways to code review such as through an email thread, pair programming, over the shoulder, or tool assistance. Through email, a programmer can easily send their colleague a file with the un-reviewed code allowing both of them to exchange regardless of schedule or location. Pair programming is when two software developers work on the same code side by side, allowing both to more fluidly exchange ideas. Developing over the shoulder is similar to peer programming except only one developer actively works on the code while the other acts as support. Tool assistance allows for even more flexibility than email chains since it allows for code review through software-based code review tools.

Code review is a very simple concept as it is simply peer reviewing in the context of software development. The importance of peer review is that another person can examine and point out someone’s mistakes unlike automated processes. Developers are still human and make human errors which are less likely to be picked up through artificial means. And solely relying on tests written by the original developer runs the risk of overlooking errors not picked up by tests or in the worst case, following the assessments of a flawed test. That’s why peer review, and by extension code review, is so important to speeding up and streamlining software development.

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

The difference between Stubs and Mocks

A mock is an object that can set an expected value, which will verify that the desired action has occurred. The stub is the object that you pass to the code under test. You can set expectations on it to work in specific ways, but those expectations are never verified. The stub’s properties will automatically behave as standard properties, and you cannot set expectations on them.

If you want to verify the code’s behavior under test, a simulation with appropriate expectations is used and validated. If you’re going to pass only values that might need to work somehow but are not the focus of the test, you can use stubs.

Important note: Stubs will never cause a test to fail. 

I believe the most significant difference is that you have written the stub with predetermined behavior. Thus, you will have a class that implements the dependencies that you have disguised for testing purposes (most likely an abstract class or interface), and a class will handle the method only through the response that is set. They don’t do anything fancy, and you’ve already written stub code for them outside of testing.

Simulations are expectations that stubs must set during testing. The simulation is not set up in a predetermined way, so you have the code to perform the simulation in your tests. The mockery is determined at run time because the code that sets expectations must be run before they do anything.

The difference between stubs and stubs

Tests written with simulation usually follow a test pattern of initialize-> set expectations -> exercise -> verify. The pre-written stub will be followed by an initialize-> exercise-> verify.

Similarities between stubs and stubs

The purpose of both is to remove all dependencies from testing a class or function so that your tests are more focused and straightforward when trying to prove it.

The most crucial difference between Stubs and Mocks is their intention. Explain this in the WHY stub and WHY simulation below

Suppose I’m writing test code for the Mac Twitter client’s public timeline controller

Here is the sample code for the test：

twitter_api.stub(:public_timeline).and_return(public_timeline_array)

client_ui.should_receive(:insert_timeline_above).with(public_timeline_array)

controller.refresh_public_timeline

Stub: The network connection to the Twitter API is very slow, which makes my tests slow. We knew it would return the timeline, so we made a stub that simulated the HTTP Twitter API so that our test could sprint, even if I were offline.


Mock: We haven’t written any UI methods yet, and I’m not sure what we need to register for UI objects. We wanted to write test code to see how my controller would work with UI objects.

In short, there is a difference between Mock and Stub objects, and RhinoMocks recognize that they allow us to write tests that better illustrate their purpose. The mock object is used to define the expectation that, in this case, I want to call the method A () with such an argument. Record and verify this expectation with ridicule. On the other hand, Stubs have a different purpose: they do not record or validate expectations but rather allow us to “replace” the behavior and state of “fake” objects to take advantage of test scenarios.

https://martinfowler.com/articles/mocksArentStubs.html

View at Medium.com

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

Stubs vs. Mocks

            Having started to learn about stubs with relation to testing in class, I went to do some further research on the functionality and usefulness of this testing strategy. I found a great deal of articles comparing stubs with mocks and evaluating their differences. I haven’t heard of mock testing before, so this was interesting to look into. Turns out, it’s pretty similar to some work we did in class. I found this great article detailing the differences which also gives a simple, understandable example.

            The main difference in stubs vs mocks is that stubs are looking to see a certain behavior, while mocks are also setting an expectation and then verifying that expectation. This is demonstrated with the use of a couple examples. Stub methods are created in the example by not doing anything but printing out “_____ is working fine.” This is to check that the correct method is being called and in the proper context. On the other hand, a mock uses a third party library (Mockito) which creates a mock Mathematics object in a test setup. Then, still in setup, the expectation is set: when(maths.sub(2,1).thenReturn(1); This is just saying 2 – 1 = 1. And finally, this expectation is verified in the test with Assert.assertEquals(1, s.subNumbers(2,1)); If this explanation seems choppy, sorry, I tried to condense 44 lines of code down to just this. Anyways, the idea is that a mock object is made, expectations are set, and then they’re verified. So both stubs and mocks are useful kind of white-box-ish testing styles, but mocks seem to have a higher level of testability. This doesn’t mean they’re necessarily better or more useful, but they have their purpose too.

From the blog CS@Worcester – Marcos Felipe's CS Blog by mfelipe98 and used with permission of the author. All other rights reserved by the author.

Transpiler

 Transpilers, or source-to-source compilers, are tools that read source code written in one programming language and generate equivalent code in another language with a similar level of abstraction. A good example of a translator is the TypeScript translator, which translates TypeScript code into JavaScript. The Babel compiler can also be used for ES6 JS code to ES5 JS code.

Compilers also translate code from one language to another, but the level of abstraction is very different between the two languages. For example, compile from .java to .class files.

ES6 and ES5

To understand the translator, you must first understand the difference between ES6 and ES5 JavaScript. ES6 (ECMAScript 6) is the specification for the next version of JavaScript. Some of its major enhancements include modules, class declarations, lexical block scopes, iterators and generators, a commitment to asynchronous programming, deconstructing patterns, and appropriate tail calls.

The features are great, but most browsers do not support the specification until now. As a result, any UI application specification written in ES6 will not work in most browsers. To run these applications, you must convert this ES6 source code to the supported JavaScript version ES5. ES5 is supported by almost all browsers and is by far the most stable version.

ES6 – Brings “types” to JavaScript. Make it closer to strongly typed languages such as Java and C#. So far, most browsers don’t support it. It must be converted to ES5 to execute in the browser.

ES5 – Over the years, we’ve been writing plain JavaScript.

Translation unit

A compiler is a program-like compiler that converts ES6 JavaScript code into ES5 JavaScript code to run in a browser. When the compiler sees an expression that uses the language functionality that needs to be translated, it generates a logically equivalent expression. The resulting expression can be very similar to or very different from the source expression.

What does a translator do?

ES6 code => ES5 code (even ES4, ES3)

Sources

https://scotch.io/tutorials/javascript-transpilers-what-they-are-why-we-need-them

https://devopedia.org/transpiler

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

7 Types of Software Testing

This week I wanted to learn more about the types of software testing that are out there. In class we already learned about methods like black and white box testing. I wanted to see if there were more methods that we may not have covered. 

This article describes 7 of the most common software testing methods. It is important to note that software testing and software development as a whole is always changing. As such there are many more than just seven software testing methods and there will definitely be new methods created in the future. These seven here are just some of the most common. 

They are:

  1. Black Box Testing
  2. White Box Testing
  3. Acceptance Testing
  4. Automated testing
  5. Regression Testing
  6. Functional Testing
  7. Exploratory Testing

Something I found interesting about this article is the relationship the author outlined between automated and regression testing. Automated testing is making your tests run automatically and regression testing is testing to verify the system still works as it did before some changes were made. Automated and regression testing go hand in hand because as software is incrementally changed you have to perform regression testing often. This is the real purpose behind automated testing – you can test repeatedly.

Another interesting method that was discussed is exploratory testing. The author refers to it as a “lazy” method of testing but also acknowledges its merits. Exploratory testing is essentially looking at a certain area of an application and exploring for any unexpected behavior. This testing method does not have any test cases. While this testing method does not really apply to the small applications that we work with in class, it may be useful in the capstone course. In that course we are creating a much larger web application with a front and backend so exploring may prove useful. 

https://usersnap.com/blog/software-testing-basics/

From the blog CS@Worcester – Half-Cooked Coding by alexmle1999 and used with permission of the author. All other rights reserved by the author.

JavaScript Testing

Because we are using JavaScript in the capstone course, I wanted to look as the tools used for testing in JavaScript, especially since I only have experience with testing in Java.

Because JavaScript is for web development, tests usually have to be run in a browser. This can be in a regular browser in an HTML page with JS scripts, or in a headless browser from the command line which is faster as there is nothing rendering onscreen. You can also run tests in Node.js. jsdom is commonly used with this to simulate a browser with pure JavaScript.

There are also many testing tools with different functions. Some tools have multiple functions and some have only one.

  1. Test launchers are used to launch your tests in the browser or Node.js with user config. (Karma, Jasmine, Jest, TestCafe, Cypress, webdriverio)
  2. Testing structure providers help you arrange your tests in a readable and scalable way. (Mocha, Jasmine, Jest, Cucumber, TestCafe, Cypress)
  3. Assertion functions are used to check if a test returns what you expect it to return and if its’t it throws a clear exception. (Chai, Jasmine, Jest, Unexpected, TestCafe, Cypress)
  4. Generate and display test progress and summary. (Mocha, Jasmine, Jest, Karma, TestCafe, Cypress)
  5. Mocks, spies, and stubs to simulate tests scenarios, isolate the tested part of the software from other parts, and attach to processes to see they work as expected. (Sinon, Jasmine, enzyme, Jest, testdouble)
  6. Generate and compare snapshots to make sure changes to data structures from previous test runs are intended by the user’s code changes. (Jest, Ava)
  7. Generate code coverage reports of how much of your code is covered by tests. (Istanbul, Jest, Blanket)
  8. Browser Controllers simulate user actions for Functional Tests. (Nightwatch, Nightmare, Phantom, Puppeteer, TestCafe, Cypress)
  9. Visual Regression Tools are used to compare your site to its previous versions visually by using image comparison techniques. (Applitools, Percy, Wraith, WebdriverCSS)

https://medium.com/welldone-software/an-overview-of-javascript-testing-7ce7298b9870

From the blog CS@Worcester – Half-Cooked Coding by alexmle1999 and used with permission of the author. All other rights reserved by the author.

C o d e C o v e r a g e T e s t i n g

Cover your tests

Welcome back, I am going to explain why you should be running most of your tests with some sort of code coverage. Code coverage is a software testing metric that analyzes how effective your tests are when ran with your code. Code coverage enables you to see the degree to which your code has been implemented. It also helps you to determine how successful unit tests are by checking the degree to which your code is covered by them. Code coverage percent is calculated by items tested by dividing elements tested by elements by elements found.

Code coverage enables you to improve the efficacy of your code by having a statistical analysis of code coverage. The test cases your currently running without code coverage may be less effective than you realize. Running with code coverage allows you to identify parts of your software that have not been examined by your test cases. This not only makes better code but it saves time because it gives you a clearer picture of where you need to refactor.

Code coverage enables you to identify parts of the software that are not implemented by a set of test cases.

How is code coverage calculated?

Well, that depends on what tools you’re using. However the common tools combine some of the following types:

  • Line coverage: percent of lines tested
  • Functions coverage: percent of functions called
  • Conditions coverage: percent of boolean expressions covered
  • Branch coverage: percent of branches executed

Are all code coverage types created equal?

When I first learned about code coverage, I assumed that line coverage must be superior, because hitting every line must be most important and probably will include most methods. While this is important it will miss out on a few things. Consider the following code from a stackoverflow answer:

public int getNameLength(boolean isCoolUser) {
    User user = null;
    if (isCoolUser) {
        user = new John(); 
    }
    return user.getName().length(); 
}

When you call this method with the boolean variable isCoolUser set to true, you will have your line coverage come back 100%. However, this method has two paths;

  1. boolean is true
  2. boolean is false.

If the boolean is true, all lines are executed, and code coverage will return 100%. What we don’t see with line coverage is that if you call this method with the boolean set to false, you will get a null pointer. Line coverage wouldn’t show you this bug, whilst branch coverage would. For these reasons, best practice is to use a coverage tool that examines multiple types of code coverage

You’ve convinced me, so how do I start?

Code coverage comes built into most IDE’s. In intellij, when you open the run drop-down menu, its two buttons below, “Run with Coverage”. I have found this tool incredibly useful/invaluable in my coding. Since learning of it, I have not run tests without using coverage. There have been a few instances where I created test classes that passed but I wasn’t aware just how terrible they could be. Using code coverage has made me learn more and see my mistakes much more easily. Lines I thought were testing the code, I could comment out and see if it reduces code coverage and learn from that. In Intelli-j’s code coverage, lines that have been executed are green while lines that aren’t are red.

Other code coverage tools are listed below:

SonarQube

JaCoCo

Clover

Selenium

Carina

Cucumber

Good coverage can’t fix bad test writing practices.

If your tests aren’t comprehensive, covering multiple possibilities, coverage won’t save you.

Thanks for taking the time to stop by and read my blog.

I learned quite a bit of information from the following links:

https://ardalis.com/which-is-more-important-line-coverage-or-branch-coverage/

https://stackoverflow.com/questions/8229236/differences-between-line-and-branch-coverage/8229711#8229711

https://www.softwaretestinghelp.com/code-coverage-tutorial/#Methodologies

From the blog cs@worcester – Coding_Kitchen by jsimolaris and used with permission of the author. All other rights reserved by the author.

Path Testing

On my last blog post, I talked about boundary class testing and equivalence class testing. I want to once again share a new method of testing that I’ve learned about recently; path testing. This is a thorough way of testing your program. Instead of testing a wide variety of possibilities using a singular input, path testing navigates through the whole program, line by line, and makes sure that every possible path has been used at least one time. This ensures that if any error occurs, you can pinpoint where in the program the error is.

A program flow graph, or control-flow graph, is a form of path testing that visualizes a program using numbers that represent each executable line of code. This directed graph shows the order of which each code statement executes. For any conditions or loops, the graph demonstrates every possible outcome. This allows someone testing the program to locate precisely where an error in the code is held. It also makes it easier to understand the logic behind the loops and conditions in the program, as it allows you to see different results that would be executed depending on the set conditions. This also goes for loops, and you can see what part of the program that the code will loop.

credit to https://www.computer.org/csdl/journal/ts/2010/04/tts2010040528/13rRUxly8UO

This is an example of what a program flow graph would look like. The graph starts off at line 1 and continues down a straight path until 4. Line 4 is where the first condition in the program is located, and we can see that it either continues to line 5,6,7 and 8, or if the condition is not met, if goes to line 10. At line 8, the program loops back to line 4 to check the condition once again.

This is a simple example of program flow graphs. It is a very useful white-box testing method that is applicable when trying to track an error in a program. Compared to boundary value testing, which is a black-box testing method, path testing is a more detailed method to check for errors. Overall they are both useful techniques to have under your belt as a software developer, and understanding how each of these test methods work means growing as a computer scientist.

Useful Sources:

https://www.geeksforgeeks.org/path-testing-in-software-engineering/
https://www.guru99.com/basis-path-testing.html
https://ifs.host.cs.st-andrews.ac.uk/Books/SE9/Web/Testing/PathTest.html#:~:text=The%20objective%20of%20path%20testing,one%20or%20more%20new%20conditions.
https://www.geeksforgeeks.org/software-engineering-control-flow-graph-cfg/

From the blog CS@Worcester – CSBlogger by mjaber54 and used with permission of the author. All other rights reserved by the author.