In the quest for AI-based autonomous testing, manual testers are often one of the first things to be replaced by machines. However, a recent study shows that augmenting human testers with AI technologies can yield better results. In this blog post, we’ll take a look at the findings of that study and explore why using AI to assist human testers is a better strategy than replacing them altogether.

What is the difference between human testers and AI-based autonomous testers?

When it comes to testing software as per Bahaa Al Zubaidi, there are two main approaches: human testers and AI-based autonomous testers. Both have their advantages and disadvantages, so it’s important to understand the difference between them before deciding which one to use.

Human testers are generally more expensive than AI-based testers, but they can provide more accurate results. This is because humans are better able to understand complex user interactions and can spot subtle bugs that might be missed by an automated system. On the other hand, AI-based testers can work around the clock and don’t require breaks, so they can complete tasks faster and more efficiently. They can also handle large volumes of data more effectively than humans can.

So, which one should you use? It depends on your budget and your needs. If you need high-quality results quickly, then an AI-based tester may be the best option. However, if you’re looking for a more thorough test with fewer false positives, then a human tester is probably a better choice. Ultimately, the decision comes down to what’s most important to you.

What are the benefits of using human testers first in the path to AI-based autonomous testing?

  • First of all, humans are better at understanding context and picking up on subtleties that machines may miss. For example, a human tester might notice that a particular feature is being used in an unexpected way and flag it for further investigation.
  • Secondly, humans can provide valuable feedback on the user experience, something that is difficult for machines to replicate.
  • And lastly, humans can help to train and calibrate AI-based testing systems, making them more accurate and reliable in the long run.

How do human testers help to train AI-based autonomous testers?

Humans have been training computers to do various tasks for decades now. In recent years, however, AI-based autonomous testers have become increasingly popular and effective. These systems are designed to mimic human behavior in order to carry out testing tasks more efficiently. In order to train these AI-based testers, human testers must first provide them with a large amount of data.

This data is used to train the AI system so that it can learn how to carry out the testing tasks. The human testers also provide feedback to the AI system so that it can improve its performance over time. In this way, human testers play a vital role in training AI-based autonomous testers.

How does using human testers first help to ensure the quality of the AI-based autonomous testing process?

One of the benefits of using human testers first is that it helps to ensure the quality of the autonomous testing process. By running tests with humans first, developers can get an accurate sense of how the AI will perform in real-world conditions. This is because human testers are able to provide feedback about the test process, including any issues or areas that need improvement.

In addition, human testers can also help to identify potential problems with the AI system that may not be apparent during development. As a result, using human testers first is an important part of ensuring the quality of the autonomous testing process.

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