AI Test Generation: Quality Beyond Coverage

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ai-coding-AI-and-test-generation-quality-creators

1. What does mutation testing verify in the context of AI-generated tests?

   1. The speed at which tests run in CI/CD pipelines
   2. How many lines of code are exercised by tests
   3. Whether the code compiles without errors
   4. That tests catch real bugs when code is intentionally modified

2. Which statement best describes engineer authority in AI test generation?

   1. Engineers should let AI decide which tests are critical
   2. Engineers are only needed to fix failing AI-generated tests
   3. Engineers should approve all tests before running them
   4. Engineers must maintain authority over critical test logic and design decisions

3. Which of these is explicitly listed as something AI cannot do in test generation?

   1. Substitute AI tests for thinking about what to test
   2. Generate tests for any programming language
   3. Generate tests that achieve 100% coverage
   4. Run tests faster than manual test execution

4. A team achieves 95% code coverage with AI-generated tests but finds that critical bugs still slip into production. What is the most likely issue?

   1. The AI model is outdated and needs retraining
   2. Coverage does not guarantee tests verify correct behavior, only that code runs
   3. The CI/CD pipeline has a configuration error
   4. The team is not using enough test assertions

5. A developer asks an AI to write tests for a function, then adds those tests without validation. Why might this approach miss bugs?

   1. The developer lacks permission to run tests
   2. AI cannot write tests for functions with parameters
   3. AI-generated tests always contain syntax errors
   4. The AI might write tests that pass but don't actually verify the function's behavior correctly

6. A team uses mutation testing and finds that 80% of their AI-generated tests fail to catch injected mutations. What should they conclude?

   1. The tests have high quality but low coverage
   2. The mutation testing tool is configured incorrectly
   3. The AI model is too old and should be replaced
   4. The tests likely don't verify actual behavior and need redesign

7. Which best captures the focus of "AI Test Generation: Quality Beyond Coverage"?

   1. It is mainly about marketing strategies for retail stores.
   2. It focuses on hardware repair and soldering circuits.
   3. It centers on test generation, quality, mutation testing.
   4. It explains how to bake bread and pastries at home.

8. Which captures a genuine tradeoff to weigh when applying these ideas?

   1. Convenience and depth are guaranteed to grow together.
   2. Speed and convenience can come at the cost of depth, ownership, or skill-building.
   3. Speed always damages a project beyond repair.
   4. There is never any tradeoff between speed and learning.

9. Who is the intended audience for this material?

   1. It is intended only for graduate researchers in physics.
   2. It targets professional chefs working in commercial kitchens.
   3. It is written exclusively for licensed pilots in training.
   4. It is written for high-school and adult learners going deeper working on ai-coding.

10. What is the responsible stance toward disclosing AI help?

    1. Hide any AI use so the work looks more impressive.
    2. Claim full credit without mentioning any tools used.
    3. Be honest about how AI was used so others can judge the work fairly.
    4. Refuse to answer if anyone asks how the work was made.

11. Which guidance is highlighted as 'AI test quality'?

    1. Treat AI output as flawless and never review it.
    2. Always agree with the first answer the model gives, no matter what.
    3. Design AI test generation focused on quality. Cover: (1) generation methodology, (2) mutation testing for quality validation, (3) engineer authority on critical tests, (4) bug catch rate tracking, (5) integration with CI/CD, (6) ongoing improvement.
    4. Skip every safeguard so things move faster.

12. Which habit is the biggest pitfall when applying these ideas?

    1. Asking for examples to make a concept clearer.
    2. Pausing to verify results before acting on them.
    3. Skipping review and trusting the first output without checking it.
    4. Comparing answers from more than one source.

13. Which statement is most consistent with the material?

    1. The topic has no bearing on day-to-day decisions.
    2. AI test generation hits coverage; quality requires deliberate design beyond coverage.
    3. Experts agree that no one should think about this issue.
    4. Every claim about this subject has been proven wrong.

14. When is it most appropriate to apply ideas from "AI Test Generation: Quality Beyond Coverage"?

    1. Only on weekends, never on weekdays.
    2. When the situation actually calls for it and you have time to think it through.
    3. Only after midnight to avoid distractions.
    4. Only when no one else is around to ask.

15. Which view of "AI Test Generation: Quality Beyond Coverage" is most consistent with a balanced take?

    1. It is a real, useful skill worth learning carefully.
    2. The ideas only matter for one specific industry.
    3. Only people with PhDs can apply the ideas correctly.
    4. It is impossible to do anything useful with the topic.