Hands-On Exercise:The Use of AI in Testing CT-AI Practice Questions (Page 1)

Questions 1–5

1. 1foundation · easy

   In a hands-on exercise, a tester uses a machine learning model to predict which modules of a software application are most likely to contain defects. This is an example of applying AI to which testing task?

   • ATest execution automation
   • BTest case generation
   • CDefect prediction
   • DRequirement analysis
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2. 2foundation · easy

   After completing an AI-based test case generation exercise, the tester compares the generated test cases against a set of predefined criteria, such as requirement coverage and expected output correctness. What is the primary purpose of this evaluation step?

   • ATo determine whether the AI approach met the exercise objectives and to identify areas for improvement
   • BTo replace the need for human testers in future exercises
   • CTo prove that AI-based testing is always superior to manual testing
   • DTo ensure the AI model is free from any biases
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4. 3application · medium

   A team is using an AI model to automatically classify bug reports into severity levels (low, medium, high, critical). The model is trained on a dataset of historical bug reports. The team wants to use this model to triage new bug reports as they are submitted. What is the most important factor to consider when applying the model to this task?

   • AThe model's classification is definitive and should be used without human review.
   • BThe model's performance on the historical dataset may not reflect its performance on new, unseen bug reports.
   • CThe model should be retrained on every new bug report to keep it up to date.
   • DThe model should be used only for bug reports from the same project it was trained on.
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5. 4foundation · easy

   In a hands-on exercise, a tester evaluates an AI-based defect prediction model by comparing its predictions to the actual defects found in a test release. Which of the following metrics would be most appropriate to assess the model's accuracy in this context?

   • ACode coverage percentage
   • BMean time to failure (MTTF)
   • CNumber of test cases executed
   • DPrecision and recall
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6. 5application · medium

   A tester is using a natural language processing (NLP) model to automatically generate test cases from user stories. The model produces a set of test cases, but the tester notices that some generated test cases are not executable because they reference undefined UI elements. What is the most effective way to improve the quality of the generated test cases?

   • AIncrease the size of the training dataset with more user stories from the same project.
   • BManually correct the undefined UI elements in the generated test cases after generation.
   • CSwitch to a different AI technique, such as a rule-based system, to generate test cases.
   • DRefine the input user stories to include more specific and unambiguous details about the UI elements.
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