Mikel  Okuneva

Mikel Okuneva

1596826800

How to Write a Minimal Unit Testing Framework in C++

For the things we have to learn before we can do them, we learn by doing them. — Aristotle

O

ver the past few months, I have occupied myself with learning data structures and algorithms for the benefit of my career. With a background in electrical engineering, they weren’t exactly my strengths. Besides, having worked in the software industry for a couple of years now, I have learned a lot by doing stuff.

As an exercise, I decided to develop a F_unctional _T_emplate _Library (Similar to STL) as my next project. This way, I could explore functional programming concepts and ways to write them in C++.

Now, if there is one thing I’ve learned from experience, it is that no matter how small your project, you always test it.

With a quick search on the internet, you’d realize that C++ isn’t short of testing frameworks. Popular frameworks such as GTestCppUnit, or Boost.Test offer rich features and allow you to write fixtures. Some even support mocks in their framework.

For a project like this, such frameworks are an overkill. All I wanted was a basic framework that I can include as a header file and not worry about building a library or linking it with the project.

So, what’s the way forward? Write one yourself!

In this post, I’ll show you how you can write a small, header-only, C++ unit testing framework in under 70 lines of code using just four macros.


Getting Started

When it comes to hiding boilerplate code, macros are your saviors. They make it easy to focus on writing tests by abstracting details about the framework.

We’ll start with an account of the four macros.

  1. BEGIN_TEST constructs a testing environment taking in a suite and a method name as arguments. You can add any prerequisites required to set up test cases here. In our case, it opens a test function and defines a boolean variable to store the result.
  2. END_TEST concludes the testing environment by returning the result of the comparison: either true or false.
  3. EXPECT_EQ compares the expected value with the actual value returned from the function under test.
  4. RUN_TEST calls a test case inside the main. You can add statements to check if the test passed by printing to the console, collect statistics, or even measure run times of your function here.
	#include <iomanip>
	#include <iostream>

	#define BEGIN_TEST(TestSuite, TestName)                                    \
	   bool test__##TestSuite##__##TestName(void)                              \
	{                                                                          \
	      bool isTrue{true};

	#define END_TEST                                                           \
	   return isTrue;                                                          \
	}

	#define EXPECT_EQ(arg1, arg2) isTrue &= (arg1 == arg2);

	#define RUN_TEST(TestSuite, TestName)                                      \
	{                                                                          \
	   bool ret = test__##TestSuite##__##TestName();                           \
	   std::cout << std::left << std::setfill('-')                             \
	   << std::setw(50) << #TestSuite " --> " #TestName " ";                   \
	                                                                           \
	   if(ret)                                                                 \
	   {                                                                       \
	      std::cout << std::setw(10)                                           \
	      << std::left << "\x1b[38;5;40m   OK \x1b[0m" /* colored in Green*/   \
	      << std::endl;                                                        \
	   }                                                                       \
	   else                                                                    \ 
	   {                                                                       \
	      std::cout << std::setw(10)                                           \
	      << std::left << "\x1b[38;5;160m   FAILED \x1b[0m" /* colored in Red*/\
	      << std::endl;                                                        \
	   }                                                                       \
	} /* Coloring valid for *nix systems. */

#testing #cpp11 #unit-testing #cpp #c

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How to Write a Minimal Unit Testing Framework in C++
Joseph  Murray

Joseph Murray

1621492530

7 Test Frameworks To Follow in 2021 for Java/Fullstack Developers

It is time to learn new test frameworks in 2021 to improve your code quality and decrease the time of your testing phase. Let’s explore 6 options for devs.

It is time to learn new test frameworks to improve your code quality and decrease the time of your testing phase. I have selected six testing frameworks that sound promising. Some have existed for quite a long time but I have not heard about them before.

At the end of the article, please tell me what you think about them and what your favorite ones are.

Robot Framework

Robot Framework is a generic open-source automation framework. It can be used for test automation and robotic process automation (RPA).

Robot Framework is open and extensible and can be integrated with virtually any other tool to create powerful and flexible automation solutions. Being open-source also means that Robot Framework is free to use without licensing costs.

The RoboFramework is a framework** to write test cases and automation processes.** It means that it may replace** your classic combo Selenium + Cucumber + Gherkins**. To be more precise, the Cucumber Gherkins custom implementation you wrote will be handled by RoboFramework and Selenium invoked below.

For the Java developers, this framework can be executed with Maven or Gradle (but less mature for the latter solution).

#java #testing #test #java framework #java frameworks #testing and developing #java testing #robot framework #test framework #2021

Mikel  Okuneva

Mikel Okuneva

1596826800

How to Write a Minimal Unit Testing Framework in C++

For the things we have to learn before we can do them, we learn by doing them. — Aristotle

O

ver the past few months, I have occupied myself with learning data structures and algorithms for the benefit of my career. With a background in electrical engineering, they weren’t exactly my strengths. Besides, having worked in the software industry for a couple of years now, I have learned a lot by doing stuff.

As an exercise, I decided to develop a F_unctional _T_emplate _Library (Similar to STL) as my next project. This way, I could explore functional programming concepts and ways to write them in C++.

Now, if there is one thing I’ve learned from experience, it is that no matter how small your project, you always test it.

With a quick search on the internet, you’d realize that C++ isn’t short of testing frameworks. Popular frameworks such as GTestCppUnit, or Boost.Test offer rich features and allow you to write fixtures. Some even support mocks in their framework.

For a project like this, such frameworks are an overkill. All I wanted was a basic framework that I can include as a header file and not worry about building a library or linking it with the project.

So, what’s the way forward? Write one yourself!

In this post, I’ll show you how you can write a small, header-only, C++ unit testing framework in under 70 lines of code using just four macros.


Getting Started

When it comes to hiding boilerplate code, macros are your saviors. They make it easy to focus on writing tests by abstracting details about the framework.

We’ll start with an account of the four macros.

  1. BEGIN_TEST constructs a testing environment taking in a suite and a method name as arguments. You can add any prerequisites required to set up test cases here. In our case, it opens a test function and defines a boolean variable to store the result.
  2. END_TEST concludes the testing environment by returning the result of the comparison: either true or false.
  3. EXPECT_EQ compares the expected value with the actual value returned from the function under test.
  4. RUN_TEST calls a test case inside the main. You can add statements to check if the test passed by printing to the console, collect statistics, or even measure run times of your function here.
	#include <iomanip>
	#include <iostream>

	#define BEGIN_TEST(TestSuite, TestName)                                    \
	   bool test__##TestSuite##__##TestName(void)                              \
	{                                                                          \
	      bool isTrue{true};

	#define END_TEST                                                           \
	   return isTrue;                                                          \
	}

	#define EXPECT_EQ(arg1, arg2) isTrue &= (arg1 == arg2);

	#define RUN_TEST(TestSuite, TestName)                                      \
	{                                                                          \
	   bool ret = test__##TestSuite##__##TestName();                           \
	   std::cout << std::left << std::setfill('-')                             \
	   << std::setw(50) << #TestSuite " --> " #TestName " ";                   \
	                                                                           \
	   if(ret)                                                                 \
	   {                                                                       \
	      std::cout << std::setw(10)                                           \
	      << std::left << "\x1b[38;5;40m   OK \x1b[0m" /* colored in Green*/   \
	      << std::endl;                                                        \
	   }                                                                       \
	   else                                                                    \ 
	   {                                                                       \
	      std::cout << std::setw(10)                                           \
	      << std::left << "\x1b[38;5;160m   FAILED \x1b[0m" /* colored in Red*/\
	      << std::endl;                                                        \
	   }                                                                       \
	} /* Coloring valid for *nix systems. */

#testing #cpp11 #unit-testing #cpp #c

Lindsey  Koepp

Lindsey Koepp

1598948520

Top 10 Test Automation Frameworks in 2020

We are moving toward a future where everything is going to be autonomous, fast, and highly efficient. To match the pace of this fast-moving ecosystem, application delivery times will have to be accelerated, but not at the cost of quality. Achieving quality at speed is imperative and therefore quality assurance gets a lot of attention. To fulfill the demands for exceptional quality and faster time to market, automation testing will assume priority. It is becoming necessary for micro, small, and medium-sized enterprises (SMEs) to automate their testing processes. But the most crucial aspect is to choose the right test automation framework. So let’s understand what a test automation framework is.

What Is a Test Automation Framework?

A test automation framework is the scaffolding that is laid to provide an execution environment for the automation test scripts. The framework provides the user with various benefits that help them to develop, execute, and report the automation test scripts efficiently. It is more like a system that was created specifically to automate our tests. In a very simple language, we can say that a framework is a constructive blend of various guidelines, coding standards, concepts, processes, practices, project hierarchies, modularity, reporting mechanism, test data injections, etc. to pillar automation testing. Thus, the user can follow these guidelines while automating applications to take advantage of various productive results.

The advantages can be in different forms like the ease of scripting, scalability, modularity, understandability, process definition, re-usability, cost, maintenance, etc. Thus, to be able to grab these benefits, developers are advised to use one or more of the Test Automation Framework. Moreover, the need for a single and standard Test Automation Framework arises when you have a bunch of developers working on the different modules of the same application and when we want to avoid situations where each of the developers implements his/her approach towards automation. So let’s have a look at different types of test automation frameworks.

Types of Automated Testing Frameworks

Now that we have a basic idea about Automation Frameworks, let’s check out the various types of Test Automation Frameworks available in the marketplace. There is a divergent range of Automation Frameworks available nowadays. These frameworks may differ from each other based on their support to different key factors to do automation like reusability, ease of maintenance, etc.

Types of Test Automation Frameworks:

  1. Module Based Testing Framework
  2. Library Architecture Testing Framework
  3. Data-Driven Testing Framework
  4. Keyword Driven Testing Framework
  5. Hybrid Testing Framework
  6. Behavior Driven Development Framework

Benefits of a Test Automation Framework

Apart from the minimal manual intervention required in automation testing, there are many advantages of using a test automation framework. Some of them are listed below:

  1. Faster time-to-market: Using a good test automation framework helps reduce the time-to-market of an application by allowing constant execution of test cases. Once automated, the test library execution is faster and runs longer than manual testing.
  2. Earlier detection of defects: The documentation of software defects becomes considerably easier for the testing teams. It increases the overall development speed while ensuring correct functionality across areas. The earlier a defect is identified, the more cost-effective it is to resolve the issue.
  3. Improved Testing efficiency: Testing takes up a significant portion of the overall development lifecycle. Even the slightest improvement of the overall efficiency can make an enormous difference to the entire timeframe of the project. Although the setup time takes longer initially, automated tests eventually take up a significantly lesser amount of time. They can be run virtually unattended, leaving the results to be monitored toward the end of the process.
  4. Better ROI: while the initial investment may be on the higher side, automated testing saves organizations many a lot of money. This is due to the drop in the amount of time required to run tests, which leads to a higher quality of work. This in turn decreases the necessity for fixing glitches after release, thereby reducing project costs.
  5. Higher test coverage: In test automation, a higher number of tests can be executed about an application. This leads to higher test coverage, which is a manual testing approach that would imply a massive team, limited heavily with their amount of time. An increased test coverage leads to testing more features and a better quality of the application.
  6. Reusability of automated tests: The repetitive nature of test cases in test automation helps software developers to assess program reaction, in addition to the relatively easy configuration of their setup. Automated test cases can be utilized through different approaches as they are reusable.

#devops #testing #software testing #framework #automation testing #mobile app testing #test framework

Software Testing 101: Regression Tests, Unit Tests, Integration Tests

Automation and segregation can help you build better software
If you write automated tests and deliver them to the customer, he can make sure the software is working properly. And, at the end of the day, he paid for it.

Ok. We can segregate or separate the tests according to some criteria. For example, “white box” tests are used to measure the internal quality of the software, in addition to the expected results. They are very useful to know the percentage of lines of code executed, the cyclomatic complexity and several other software metrics. Unit tests are white box tests.

#testing #software testing #regression tests #unit tests #integration tests

Tamia  Walter

Tamia Walter

1596754901

Testing Microservices Applications

The shift towards microservices and modular applications makes testing more important and more challenging at the same time. You have to make sure that the microservices running in containers perform well and as intended, but you can no longer rely on conventional testing strategies to get the job done.

This is where new testing approaches are needed. Testing your microservices applications require the right approach, a suitable set of tools, and immense attention to details. This article will guide you through the process of testing your microservices and talk about the challenges you will have to overcome along the way. Let’s get started, shall we?

A Brave New World

Traditionally, testing a monolith application meant configuring a test environment and setting up all of the application components in a way that matched the production environment. It took time to set up the testing environment, and there were a lot of complexities around the process.

Testing also requires the application to run in full. It is not possible to test monolith apps on a per-component basis, mainly because there is usually a base code that ties everything together, and the app is designed to run as a complete app to work properly.

Microservices running in containers offer one particular advantage: universal compatibility. You don’t have to match the testing environment with the deployment architecture exactly, and you can get away with testing individual components rather than the full app in some situations.

Of course, you will have to embrace the new cloud-native approach across the pipeline. Rather than creating critical dependencies between microservices, you need to treat each one as a semi-independent module.

The only monolith or centralized portion of the application is the database, but this too is an easy challenge to overcome. As long as you have a persistent database running on your test environment, you can perform tests at any time.

Keep in mind that there are additional things to focus on when testing microservices.

  • Microservices rely on network communications to talk to each other, so network reliability and requirements must be part of the testing.
  • Automation and infrastructure elements are now added as codes, and you have to make sure that they also run properly when microservices are pushed through the pipeline
  • While containerization is universal, you still have to pay attention to specific dependencies and create a testing strategy that allows for those dependencies to be included

Test containers are the method of choice for many developers. Unlike monolith apps, which lets you use stubs and mocks for testing, microservices need to be tested in test containers. Many CI/CD pipelines actually integrate production microservices as part of the testing process.

Contract Testing as an Approach

As mentioned before, there are many ways to test microservices effectively, but the one approach that developers now use reliably is contract testing. Loosely coupled microservices can be tested in an effective and efficient way using contract testing, mainly because this testing approach focuses on contracts; in other words, it focuses on how components or microservices communicate with each other.

Syntax and semantics construct how components communicate with each other. By defining syntax and semantics in a standardized way and testing microservices based on their ability to generate the right message formats and meet behavioral expectations, you can rest assured knowing that the microservices will behave as intended when deployed.

Ways to Test Microservices

It is easy to fall into the trap of making testing microservices complicated, but there are ways to avoid this problem. Testing microservices doesn’t have to be complicated at all when you have the right strategy in place.

There are several ways to test microservices too, including:

  • Unit testing: Which allows developers to test microservices in a granular way. It doesn’t limit testing to individual microservices, but rather allows developers to take a more granular approach such as testing individual features or runtimes.
  • Integration testing: Which handles the testing of microservices in an interactive way. Microservices still need to work with each other when they are deployed, and integration testing is a key process in making sure that they do.
  • End-to-end testing: Which⁠—as the name suggests⁠—tests microservices as a complete app. This type of testing enables the testing of features, UI, communications, and other components that construct the app.

What’s important to note is the fact that these testing approaches allow for asynchronous testing. After all, asynchronous development is what makes developing microservices very appealing in the first place. By allowing for asynchronous testing, you can also make sure that components or microservices can be updated independently to one another.

#blog #microservices #testing #caylent #contract testing #end-to-end testing #hoverfly #integration testing #microservices #microservices architecture #pact #testing #unit testing #vagrant #vcr