What is the main goal of coverage optimization in AI-based test generation?

To ensure every test case covers exactly one requirement

To eliminate all redundant test cases, even if coverage is reduced

To generate as many test cases as possible regardless of coverage

To achieve high coverage of code, requirements, or behavioral models with a minimal set of test cases

A team is testing a system with a complex state space. They have a formal model but it is incomplete. They want to generate test cases that cover both the model and unexpected states. Which approach is best?

Use only model-based generation from the incomplete model

Use a generative model to create random state transitions

Combine model-based generation with search-based exploration to cover both specified and unspecified states

Use only search-based generation with code coverage as the fitness function

How can AI-based input data generation help in testing edge cases?

By removing the need for boundary value analysis

By randomly selecting inputs from a uniform distribution

By ensuring that all test inputs are within the normal range

By learning from existing data to produce inputs at the boundaries of valid input ranges

What is a key consideration when integrating AI-based test case generation into an existing test process?

Ignoring the existing test data and starting from scratch

Ensuring the generated test cases can be executed in the existing test framework

Replacing all manual testing with AI-generated tests

Requiring the AI to generate tests only for new features

Certified Tester AI Testing

Domain 11Objective 3

Using AI for Test Case Generation CT-AI Practice Questions (Page 3)

Part of the Domain 11: Using AI for Testing domain, which makes up ~11% of our current practice bank. ISTQB does not publish an official question count, but from its 60-minute exam (~25–40 total, ~3–4 in this domain), expect 1–1 from this objective — we provide 24 practice questions to prepare you well beyond it. (estimate)

24questions here

5free pages

5concepts

Questions 11–15

11foundation · easy
What is the main goal of coverage optimization in AI-based test generation?
Select an answer first
12expert · hard
A team is testing a system with a complex state space. They have a formal model but it is incomplete. They want to generate test cases that cover both the model and unexpected states. Which approach is best?
Select an answer first
13foundation · easy
How can AI-based input data generation help in testing edge cases?
Select an answer first
14application · medium
After generating test cases with AI, a team wants to assess how well the tests cover the requirements. Which metric is most appropriate?
Select an answer first
15foundation · easy
What is a key consideration when integrating AI-based test case generation into an existing test process?
Select an answer first

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