Benefits of AI in Quality Monitoring

Posted By :Sanjana Singh |31st August 2021

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As the world advances in the digital revolution, anticipating market needs in advance and creating a commendable and predictable system to deal with future situations is on the horizon. 

In the present case, testing needs help speeding up delivery. AI in quality assurance can help us get there. 


Benefits of Using AI Technology for Quality Monitoring and Control


Expedited Timelines

There are many ways our team of developers speeds up the process of developing an application. Canceling interruptions, including the testing process, are one of them. Instead of passing thousands of lines of code, the AI will be able to sort through log files, scan the codes, and detect errors in seconds. In addition, AI does not have burnout syndrome and thus produces better and accurate results. 

Also, AI can come up with code changes. It can adapt and define new functions and can be considered to determine if something is a new feature or an error from a change of code.


Well-researched Release

Using Artificial Intelligence in Quality Assurance makes it possible to test similar apps and see what has contributed to their success. After understanding the needs of the market, new test cases can be made to ensure that the application does not break when it comes to achieving specific goals.


Experimental Test Performance

In the meantime, a reasonable amount of time for a QA expert is planning on setting up test cases that will enable them to present the application. The same procedure should be used every time a new version is released on the market. 

Experimental AI input tools can help testers analyze an app by crawling across the screen and producing, using their test conditions, and saving time for editing.


Expended Tester's Role

With AI entering the picture, teams of QA engineers will find themselves learning new skills. They will need to develop their neurolinguistics programs, business intelligence, mathematical modeling, and algorithmic analysis skills.


The role of AI in quality assurance: Autonomous QA test cases

  • The AI builds test cases on the journey of predetermined and regular users and then checks the observations around the bugs.
  • AI explores how one cannot think and cleverly clears checks to increase coverage.
  • AI code modules contain and create experiments to test quirks for specific developers and historical bug patterns.
  • When we talk about the AI test cases in Quality Assurance, here are some ways that testers are using AI right now.
  • AI implementation with image-based testing.
  • To determine whether a test is valid for testing.
  • Uses AI spidering
  • Testing Test API
  • Default functions
  • With the active involvement of AI and machine learning at Certified Quality, the future will explore QA experts.


The Six Levels of AI Testing


Zero Level: At this stage, coding is a recursive process. Adding a field to a page will mean adding to the test and looking at all the elements and forms in a new experiment.

The more tests you take, the more likely you are to fail to work when fully functional. To solve this, you check all failed tests to determine if something is a bug or a new base.


First level: At this level of testing, you will need AI algorithms that can determine non-linear changes and which ones.

Modern AI technology can help you with writing test code by writing checks. Also, they can check if they pass the test. And if it fails, it should let you know so testers can check if the failure is real or has occurred due to a software change.


Second Level: At this level, AI understands the difference depending on what app users will know and understand. Therefore, it will be able to schedule changes from multiple pages because it understands them well.

At level 2, the AI can tell the testers if the changes have been made and ask whether they should accept or reject the changes as a group.


Third Level: By using machine learning techniques, AI can evaluate the physical properties of an application and determine whether the UI is off based on design law.

AI's at this level can explore pages that introduce human intervention simply by understanding the details of the data and designs. It can look at hundreds of results and analyze how things change over time. After that, with the help of machine learning, you'll be able to see the difference in the changes.


Fourth Level: AI can drive tests. AI can detect user interaction over time and see an interaction, page comprehension, and user flow.

When AI understands the page type, it will automatically use reinforcement learning techniques to start driving tests.


Fifth level: At this stage, the AI will communicate with the product manager, understand the application, and run tests - all in one.

While currently, AI is still at level 1, there are automated scenarios already using Artificial Intelligence: Visual UI testing, API testing, automated testing, and Spidering. 



Artificial Intelligence and technology are aspects of life that always fascinate and amaze us with new ideas, topics, methods, products… etc. AI has not yet been used as a standalone film (intelligent robots), but there are many essential attempts to reach the level and competition in the market, just like the other robots they show on TV. However, hidden projects and developments in industrial enterprises.

Finally, we were in this study with the definitions of AI, a brief history, the use of public AI, the use of military AI, the ethical principles of AI, and the three laws of robots. This is not the end of AI; there is a lot to come from it. Who knows what AI can do for us in the future? Maybe it will be a whole community of robots automating most of the tasks.



About Author

Sanjana Singh

Sanjana is a QA Engineer with skills in Manual Testing and always eager to learn new technologies.

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