Scope of Test Automation with ML and AI

Posted By : Sanjana Singh | 26-Dec-2020

                                                  
                                                Image downloaded from: https://www.testing-whiz.com/media/2899/top-5-test-automation-synopses-that-leverage-artificial-intelligence.png

Machine Learning in Exploration Machine Learning (ML) is a technology based on pattern recognition. Algorithms analyze tones of information and identify guess patterns. This way it changes the default test method, as ML does not require a test interface. Most automated QA is a back-end process. UI testing. The use of ML bots can be useful while processing the end user experience. Most modern applications have similar patterns in design, functionality, or interface. For example, you can easily find a shopping cart, a product filter, and a payment window in an online store. Bots can be trained in a particular area of the software to use more test cases than postponed testing. Image-based testing using visual authentication tools is a characteristic pattern that ML sees. A QA engineer can create a simple machine learning test that will automatically detect visible defects in the software. APIs. ML test attempts help perform successful API layer testing. Algorithms take the analysis of test texts, while the tester is not caught in making multiple API calls. Strategic direction. Usually, QA engineers use the entire testing system due to minor changes within the code. The use of ML tools enables you to define the minimum number of tests required to further correlate code correction. It also analyzes vulnerable software sites and current installation installs.
 

Why we need ML in Test Automation
 

Organizations today are moving toward digitalization that is becoming increasingly important. In line with that, the advent of Cloud, DevOps, Internet of Things (IoT), etc. It has made it difficult to operate and test automation.

As a result, software and applications need to be upgraded continuously and quickly. This makes the task of testing the software more complicated.

Interestingly, to deal with the complex and fragmented software testing process, intelligent testing automation can be very helpful. As the testing process is complex and time-consuming, it is important that we go digital.

ML offers many tools and technologies such as machine analysis and learning. This helps to make the test automation real. In addition, ML tools provide guidelines and recommendations for conducting experiments while reading and developing software development stages. It helps to predict business needs and results in helping coding teams work better.


The impact of Selenium on the future of software testing


Software testing is constantly changing, but it continues to face new challenges. The automation of testing has proven to be an important solution for testers who face many challenges with manual testing. Open source innovation, DevOps, and agile development have played a major role in the continuous grip of the experimental environment. In this space, Selenium has built a reputation for being the perfect automation tool for continuous development and delivery. It offers many benefits including compliance, cost-effectiveness, speed, and more.

There are many reasons why Selenium is still regarded as a promising tool for changing the future.

  • Open and Free Source: Based on open source framework and can be downloaded by anyone. Other than that, no recurring fees are involved. The free form of this platform makes it possible for long-term observers and participants to be considered as this reduces the cost of testing. Everyone can access and benefit from it, including small to large businesses and independent quality analysts.
     
  • Multi-Operating System & Support Browser: Selenium supports all the most active programs and web browsers. This allows analysts to evaluate the performance of their applications across all popular platforms. These include Linux, Windows, Mac OS, Solaris, HtmlUnit, Android, and Firefox and Firefox, Chrome, IE, Opera, and Safari applications.
     
  • Adaptability to programming languages: Examiners can choose the language in which they want to write texts. Selenium supports many languages including Java, Perl, Python, Ruby, C #, and PHP among others. You may select a client API depending on the programming language.
     
  • Selenium IDE: Selenium IDE has powerful tools that allow you to record, correct, and schedule tests. There are many other reasons that give Selenium on the brink of making an impact on future testing.
     
  • Source: Artificial Intelligence Impact On Software Testing - QA Madness Software testing company
     
  • Automatic testing with AI: The need for new adoption Automatic testing is of paramount importance in the Agile age because it facilitates faster product execution. Empowered with their “learning” capabilities, AI-powered automation test tools bring a level of automation to the table that simple law-based automation can achieve.
     

                                                 
                                         Image download: https://www.immuniweb.com/images/automated-penetration-testing/ai-and-machine-learning-for-automated-penetration-testing.jpg
 

Whether you are an IT business leader or explorer, you should be open to new AI-enabled adoptions in the world of automated testing. See how Testsigma uses AI technology to keep track of changes, recommend affected tests, fix (cure) and make suggestions for test failures.


Artificial Intelligence (AI) and Learning Equipment (ML) are the latest buzz in the field of digital transformation. But are they ready to replace and take Test Automation made with Selenium? Let's find out!

Selenium is known as a web-based testing tool. It is an open source tool that is widely used compared to other licensed ones. It, being an open source, portable source; can be easily used to create applications automatically.
 

Selenium features are as follow:

  • It is very flexible in retrospect and active testing. Regression tests are more helpful in performing tests compared to modified applications. This may determine the validity of these modified codes. While, performance testing helps test business needs compared to software and ensures there is no error with the software.
     
  • These tests can be written in a variety of programming languages, for example, C ++, Python, Ruby, and Java.
     
  • Selenium easily supports browser testing. This enables the use of test cases in multiple browsers multiple times.
     
  • Both AI and Learning Machines not only simplify flexible software but also contribute to the more complete construction of the technology sector. Since the conception of AI / ML, the future of Selenium Test Automation looks like a sunset. Although they both have different features to support their existence, it is hard not to look at Mechanical Learning to find something progressive in technological development.
     

How machine learning can change the face of automated testing by preventing other cases:

  • The manual labor of writing test cases can be reduced.
     
  • When this happens, the test becomes brittle, which can lead to false failure due to skipping steps or a drop in other tests.
     
  • Use cases for ML-based test automation.
     
  • Organizations cannot and should not completely change their testing strategy tested in ML. Development and evaluation teams should assess whether ML-based testing is appropriate for them, with clear KPIs and performance metrics demonstrated.

Use cases for automation testing with Machine Learning:

  • Remove specific, soft test texts
     
  • Give business testers an alternative to automated testing
     
  • Increase the default auto test
     
  • Accelerate the time to perform and maintain test automation
     
  • Increase the default auto test

If you switch to manual testing with ML-based test automation, you will likely increase the installation of automatic testing and reduce the risk of errors running into production. That's great, but you still have to make sure your team is working well and driving value. Properly provide ML-based test automation suite with team members to avoid duplication and focus on problem areas.

You should also consider how the teams will view the results of the two approaches. Teams should strive to look at a consistent quality dashboard that shows both automated audit reports with one view so that managers can assess the overall product quality easily.
 

The future of ML-based test automation

You can expect more changes in the default ML checkpoint in the next few years. First, ML tools are emerging, and over the next two to two years it is important for DevOps teams to adopt, integrate and modify their processes to bring these tools to their SDLC.

Teams will need new ways to decide when to use ML based on traditional code-based options. 

Finally, ML tools will be flexible to cover additional types of testing in addition to performance tests, such as safety tests.

I recommend exploring how ML-based test automation can complement existing code-based practices, and identify the top challenges these tools will face better. Ideally, using ML-based test automation in the new decade can dramatically increase the value of your software cycle.
 

Conclusion

One area of Selenium testing that often fails is when developers make any changes to the software. These changes can be as simple as renaming a field ID. Solutions based on Selenium uses a single selector or one method that relies on one method for finding fields in the software. Automatic AI-based automation tools can use advanced machine learning to automatically adapt to such changes. The result is that the tests are easier to maintain and more reliable.

Therefore, machine learning has made significant progress in this industry. However, there is still a long way to go before it can be completed. To bring it to the right level of accuracy, this technology still requires the intervention of human intelligence. Despite its limitations, its future looks bright compared to Selenium - except that the latter also incorporates AI and changes with time. It can be when both are present and use the best. Time will tell how this will change in the coming years, but the look of the world looks promising and exciting.

ML algorithms alone cannot perform the function of web browser automation, but we can make the system more efficient and reliable by working with traditional web automation frameworks. Although web automation has made great strides in learning, the best solutions are yet to come.

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