Six levels of AI Test

 

Six levels of AI Test

zero:

At this stage, writing code is an iterative process. Adding a field to the page mean adding to the test. adding a test means. Adding a form to a page means adding a test that checks all fields. Adding a Page means looking at all components and shapes thanks to a whole new test.

 

More tests, the greater the instances you do not guarantee the functionality of the application as a whole. To solve this problem, you check all the tests do not know if something is a bug or a new base.

 

First level:

At this level, better AI sees your application, your QA become more autonomous. AI should not just look at the model of the object of the page document, but also its visual image. Once the test framework sees holistically page, it will help you write checks that you have to write otherwise manually.

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For this level test, you need AI algorithms that can determine what changes are not made changes and are.

 

Today's AI technology can help you write the test code by writing checks. In addition, they can check whether a test passes. And if it fails, it must warn you so that you are able to check whether the failure is real or past due to a change in software.

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Level two:

With a level, quality assurance specialist avoid the appearance of taking time writing checks, while you can also use the AI ​​to test the visual elements of the page. But the following: - check each test to fail - is a tedious task.

 

At this level, your AI understands the differences in the application users would also be able to understand. Thus, it will be able to group changes from a certain number of pages, as he understands them semantically.

 

At level 2, AI can tell the tester when the changes are the same and whether to accept or reject the changes as a group.

 

Level three:

At one level, human intervention is still required for any change vetting or failure detected in the application. At level 3, AI does the job.

 

For example, using Machine learning techniques, AI can examine the visual elements of the application and decide if the user interface is off, on the basis of the standard rules of design.

 

The AI ​​at this level can evaluate the pages less human intervention, simply by understanding the data and design rules. He would look at hundreds of results and analyze how things change over time. Then, with the help of machine learning, it would be able to identify differences in the changes.

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Level four:

So far, humans have always been the driving tests. Level 4 is where AI would resume.

 

Since Level 4 AI is able to consider a semantically application and understand as a human being would be, it can lead testing. The AI ​​will be able to see user interactions over time and visualize the interaction, understanding the page and user flow.

 

Once AI includes the type of page it will use learning techniques to start building the test drives, automatically.

Level five:

This part is a novel of science right now. At this stage, the AI ​​will be able to communicate with the product manager, understand the application and conduct the tests - by himself.

 

While present, AI is still at level 1, there are automation events that are already using artificial intelligence: visual user interface testing, API testing, automated testing and Spidering.

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Talk to our quality assurance experts

 

The framework for testing AI and platforms

While slow, but quality assurance has been input and the prevalence in the third wave of Automotion with the help of quality assurance platforms AI.

 

Some automation test high operational AI tools on the market today

 

 

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AI Test Framework and platforms

 

1. Eggplant AI.

It uses intelligent algorithms for navigation software, predict many defects, and solve challenges with the help of advanced data correlation. It also allows automation of test automation engines and provides a graphical analysis of the test coverage and results.

 

2. Appvance.

The platform provides in-depth analysis software through learning of the machine and offers models "plans applications" that apply cognitive generation. These plans include the ability to develop multiple test cases in minutes. Appvance also comes with a Test Designer feature that can be combined with a comparison screenshot, data from tests conducted, and the automatic capture AJAX or DOM.

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3. Testim.io

It uses machine learning for the creation, execution and maintenance of automated tests. It focuses on functional end to end and test the user interface. The platform is continually smarter and stability of its test suites increases with more points.

 

4. Testsigma.

It is one of the most commonly used tools AI controlled for continuous automated testing. The platform uses the processing of natural language tests for writing automated quality testing. It also identifies cases of relevant tests for testing and records the sudden test failures.

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Challenges in the quality assurance process and combination AI

Although the future of AI and quality management belong together, there are many obstacles that must be overcome to 100% adopted in software testing. Some of these challenges -

 

Data management is not a public process

Processing of unstructured terabytes of data requires a lot of manpower and financial support. Today, there are only a handful of companies that have the capacity to handle heavy data, let alone analyze and prepare for learning machine.

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The lack of expertise AI

Artificial intelligence, as a skill, is still at a nascent stage. This makes finding the expertise that can take control of a complex process such as application testing is extremely difficult. And even if you manage to find the resource, it would cost a lot for a starting level of business to handle.

 

The solution to this may be to entrust the task to a quality assurance expert offshore.

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