Will AI Perform Testing?

 This topic becomes more important in the world context driven by current technology where a large number of software companies continue to adopt top-class technology such as AI and ML. In this post, we have tried to find out that AKA can do testing ever and if not, how it will increase various aspects of testing.

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Testing is an important process that ensures customer satisfaction on the application and helps prevent potential failures that can bring some negative aspects in the line. This is a planned process where the application is reviewed and analyzed under certain conditions to understand the overall threshold and hazards involved in its application.

By assimilating the machine that can mimic human behavior carefully, testers can certainly move beyond the traditional path of the manual testing model and move forward progressively towards a precision and automatic-based sustainable testing process. The AI-Powered testing platform is able to recognize changed controls more efficiently compared to humans and with sustainable updates for algorithms, a few changes can be observed.

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At present, the main challenge comes from increasing complexity, both in the system, as well as, system landscapes, and it requires more testing. Also, testing must be faster when we experience more frequent shipping. Therefore, we must be more efficient and that means that we need to use as much tools and technology as much as possible. Business intelligence has been around for a long time in different areas, but not much in the testing landscape. But today, we must try to take advantage of business intelligence support for testing. We must try to implement AI in areas where he can perform better than humans and let the testers do what humans are better.

You might be wondering, what tasks should we let AI do? The machine is usually good at carrying out repetitive tasks as long as they come clearly what to do and what results are expected. For example, analyzing a large number of data is something that is smart, and with ml capabilities, it can support the analysis done by humans for whom it is not possible through the amount of data.

On the other hand, humans understand other humans are better because nuances in human communication are something very difficult for AI to be understood. Humans are also better in adapting communication based on signals from listeners. So, it might take a long time for AI-powered machines to understand subjective knowledge and communicate well to stakeholders.

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These are some ways AI will have an impact on the testing domain.

It will change the perspective of testing and thus, will make the testing life much easier. We can hope to see trends where human testers must do a large amount of mechanical dirty work that rotates around executing, implementing, and analyzing the test results. However, they will still be an integral part of the entire testing process to approve and act under the findings.

Another important thing to understand is that the problem is resolved with AI is not deterministic and they change when the system combines new data. There will be an array of possible results and testers need to run the test several times to ensure that the conclusion is very statistically correct. This testing method sounds more provoking thinking, more experimental, and more mathematics.

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Another major aspect that is often discussed is how the AI ​​powered system will ensure the truth of what is being tested. Humans achieve this by finding a different source of truth - a stakeholder, product owner, customer etc. Testers will need a series of different skills to develop and maintain a test suite empowered by AI who tested AI-powered products. Requirements will involve more focused on skills related to science and data testers