Category Archives: Blog-4

Testing with Mockito

During class, we have been doing many testing methods including Junit 5 and for the past two weeks we have been using Mockito, so for today’s blog, I want to focus more on the framework and testing with Mockito. Before we get started let us talk about what is mocking. Mocking is a process of developing the objects that act as the mock or clone of the real objects. In another word, mocking is a technique where mock objects are used instead of real objects. The purpose is to isolate and focus on the code being tested. Mock uses objects such as Fakes, Stubs, and mocks. A fake object has working implementation but takes shortcuts which makes them not suitable for production. Stub object usually does not respond to anything outside that is programmed in for the test. Mocks are objects that are preprogrammed with expectations which form a specification of the call that is called to receive.

Now let us move on to what Mockito is, Mockito is a java based mocking framework that internally uses the Java Reflection API to generate mock objects for a specific interface. Mock objects as I explained previously are used as dummy objects. The main purpose of using the Mockito framework is to simplify the development by using mocking external dependencies and use them in the test code. Few benefits of Mockito include writing beautiful tests with a clean & simple API, it creates mock objects using annotations like for example @Mock, and In Mockito, there is no requirement for writing your mock objects. The first link I have provided gives a lot of detailed examples of Mockito. It has helped me to learn the framework fully. I highly recommend to check it out and follow the example.

https://www.vogella.com/tutorials/Mockito/article.html

https://site.mockito.org/

https://www.javatpoint.com/mockito

From the blog Derin's CS Journey by 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 Unknown 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 Unknown and used with permission of the author. All other rights reserved by the author.

The Long Road

 When working on an open-source project, get in the habit of downloading the latest version of the code (preferably from their source control system) so you can review its history and track future developments. Take a look at the structure of the codebase and think about why the code is organized the way it is. Take a look at the way developers organize their code modules to see if it makes sense, and compare it to the way they might have used it. Try to refactor the code to understand why its coders made the decisions they did, and think about what the code would look like if you were the one coding it. Not only will it give you a better understanding of the projects; Also make sure you can build those projects. If you’ve found a better way to do something, you’re ready to contribute code to the project. Inevitably, as you go through the code, you’ll come across decisions you completely disagree with. Ask yourself if the developers of the project might know something you don’t or vice versa. Consider the possibility that this is a legacy design that needs to be refactored; And consider whether making a toy implementation for the relevant feature would help clarify the issue.

You end up with a toolbox filled with all sorts of quirks that you’ve collected from other people’s code. This will hone your ability to solve small problems more quickly and quickly. You’ll be able to tackle problems that others think are impossible to solve because they don’t have access to your toolbox. Take a look at the code for the Git distributed source control system written by Linus Torvalds, or any code written by Daniel J. Bernstein (known as DJB). Programmers like Linus and DJB occasionally make use of data structures and algorithms that most of us have never even heard of. They’re not magicians — they’ve just spent their time building bigger and better toolboxes than most people. The great thing about open source is that you can look at their toolbox and make their tools your own. One of the problems in software development is the lack of teachers. But thanks to the proliferation of open-source projects on sites such as SourceForge. Net and GitHub, you can learn from relatively representative code examples from the world’s programmer community.

In ODS, Bill Gates says: “The most subtle test of programming ability is giving the programmer about 30 pages of code and seeing how quickly he can read through it and understand it.” He realized something very important. People who quickly learn directly from the source code will soon become better programmers because their teachers are the lines of code written by every programmer in the world. The best way to learn patterns, idioms, and best practices is to read the open-source. Look at how other people to code. It’s a great way to stay relevant, and it’s free. — Chris Wanstrath at Ruby 2008 [

Pick an open-source project with deep algorithms, such as Subversion, Git, or Mercurial source control system. Browse through the project’s source code and jot down any algorithms, data structures, and design ideas that seem novel to you. Then write a blog post describing the structure of the project and highlighting the new ideas you’ve learned. Can you find a situation in your daily work where the same idea can be applied?

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

The Long Road

 When working on an open-source project, get in the habit of downloading the latest version of the code (preferably from their source control system) so you can review its history and track future developments. Take a look at the structure of the codebase and think about why the code is organized the way it is. Take a look at the way developers organize their code modules to see if it makes sense, and compare it to the way they might have used it. Try to refactor the code to understand why its coders made the decisions they did, and think about what the code would look like if you were the one coding it. Not only will it give you a better understanding of the projects; Also make sure you can build those projects. If you’ve found a better way to do something, you’re ready to contribute code to the project. Inevitably, as you go through the code, you’ll come across decisions you completely disagree with. Ask yourself if the developers of the project might know something you don’t or vice versa. Consider the possibility that this is a legacy design that needs to be refactored; And consider whether making a toy implementation for the relevant feature would help clarify the issue.

You end up with a toolbox filled with all sorts of quirks that you’ve collected from other people’s code. This will hone your ability to solve small problems more quickly and quickly. You’ll be able to tackle problems that others think are impossible to solve because they don’t have access to your toolbox. Take a look at the code for the Git distributed source control system written by Linus Torvalds, or any code written by Daniel J. Bernstein (known as DJB). Programmers like Linus and DJB occasionally make use of data structures and algorithms that most of us have never even heard of. They’re not magicians — they’ve just spent their time building bigger and better toolboxes than most people. The great thing about open source is that you can look at their toolbox and make their tools your own. One of the problems in software development is the lack of teachers. But thanks to the proliferation of open-source projects on sites such as SourceForge. Net and GitHub, you can learn from relatively representative code examples from the world’s programmer community.

In ODS, Bill Gates says: “The most subtle test of programming ability is giving the programmer about 30 pages of code and seeing how quickly he can read through it and understand it.” He realized something very important. People who quickly learn directly from the source code will soon become better programmers because their teachers are the lines of code written by every programmer in the world. The best way to learn patterns, idioms, and best practices is to read the open-source. Look at how other people to code. It’s a great way to stay relevant, and it’s free. — Chris Wanstrath at Ruby 2008 [

Pick an open-source project with deep algorithms, such as Subversion, Git, or Mercurial source control system. Browse through the project’s source code and jot down any algorithms, data structures, and design ideas that seem novel to you. Then write a blog post describing the structure of the project and highlighting the new ideas you’ve learned. Can you find a situation in your daily work where the same idea can be applied?

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