The Role of AI in testing

Hello all, Thamsil Ahmed here. In this blog, I would like to share with you some of my expertise as a software tester by focusing on AI’s role in the testing field.

Artificial Intelligence or AI has completely changed the way of how certain things are performed. From basic things like shopping to getting medical care, AI has played a major role in enhancing the overall experience. In this article, I would like to talk about how AI has transformed the world of testing and its overall enhancement.

Talking about AI in testing involves all those automated tools that make testing interesting and more efficient. I have talked about some of the automation tools used for testing in one of my previous articles. But how is AI integrated into these tools is something I would like to talk about here.

Basically, AI employs various techniques like machine learning, NLP, bots, etc. is one of the AI understanding and working on improving those methods that can make any process efficient and effective. In the field of testing, AI helps to:

  • Run or perform automation tests based on the best methods, so that the developers or testers can focus on the other crucial tasks. Here, time and effort are saved as a result of integration.
  • When it comes to User Interface (UI) testing, the integration of ML makes to find the bugs missed out by an actual tester, as it used Image Recognition techniques to detect UI-related bugs.
  • A very crucial feature is the generation of datasets that will be required to perform certain test scripts that require data to be entered. This is achieved by an ML model that learns existing datasets and generates similar information.
  • Helps in creating high-quality test cases or scripts.
  • Identification and eradication of duplicate test cases.
  • The different algorithms in AI help in preventing those tests from breaking when changes are made in the code. Very often, tests that worked fine before the change in the source code break as sometimes they are not compatible with the new source code. This technique is known as self-healing, where the fix is automatic.
  • The test results are more accurate when AI is integrated as AI methods are trained on the expected results.
  • Multiple tests can be performed at the same time, thus saving time and effort.
  • Enhances Application Program Interface(API) testing, and ensures that the backend and frontend activities are in sync, with AI playing a role in detecting any deviation from the normal exchange of information or communication between the backend and frontend.
  • Natural Language Processing (NLP) when used to write test cases, reduces the creation time of a test as it is performed in a language easily comprehended by humans.

Thank you for reading!

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