Using AI to Test the GUI CT-AI Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is a common challenge when applying AI to GUI testing?

   • AAI eliminates the need for any test maintenance
   • BAI testing tools are always faster than manual testing
   • CAI models require large amounts of high-quality training data to perform well
   • DAI testing does not require any human oversight
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2. 17application · medium

   A team wants to generate GUI test cases that cover all major user workflows in a web application. They have a detailed state model of the application. Which AI-based approach is most appropriate?

   • AUse NLP to parse the state model and convert it into test scripts.
   • BUse exploration-based testing to randomly explore the application without a model.
   • CUse computer vision to analyze screenshots and generate test cases from the visual layout.
   • DUse model-based testing to generate test cases from the state model.
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4. 18expert · hard

   A team has a large suite of GUI tests that use CSS selectors. After a major UI overhaul, many selectors are broken. The team wants to use self-healing to reduce maintenance, but they are concerned that self-healing might mask real defects. What is the best way to mitigate this risk?

   • ADisable self-healing and manually update all selectors.
   • BConfigure self-healing to only apply when the new element is visually similar to the original.
   • CUse visual validation to automatically approve all healed elements.
   • DUse self-healing without any constraints to maximize automation.
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5. 19foundation · easy

   Which of the following is a common challenge in GUI testing that motivates the use of automation?

   • AThe GUI is often subject to frequent changes, making manual regression testing time-consuming
   • BGUI testing is only needed for mobile applications
   • CGUI testing can be fully performed by unit tests
   • DGUI testing does not require any test data preparation
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6. 20application · medium

   A team wants to automatically explore a new web application to discover untested user flows and generate test cases without prior knowledge of the application's structure. Which AI-based approach is most appropriate?

   • AUse exploration-based testing where an AI agent autonomously navigates the UI and records actions.
   • BUse model-based testing to create a state model from the requirements and generate tests from it.
   • CUse NLP to parse the user manual and generate test cases from the described steps.
   • DUse visual validation to compare screenshots of different pages and generate tests from the differences.
   Reveal Solution Discussion Select an answer first