Using AI for the Optimization of Regression Test Suites CT-AI Practice Questions (Page 5)

Questions 21–25

1. 21expert · hard

   A team has a regression suite of 5,000 tests. They want to use AI to select a subset of tests for each code change. They have historical data on test failures and code changes. They also have a constraint: the selected subset must run within 30 minutes. Which approach would best balance fault detection and the time constraint?

   • ASelect all tests that have ever failed in the past
   • BSelect tests that cover the most recently changed modules, regardless of time
   • CTrain a model to predict the probability of each test failing, then select the highest-probability tests until the 30-minute budget is reached
   • DRandomly select tests until the 30-minute budget is reached
   Reveal Solution Discussion Select an answer first
2. 22expert · hard

   A team has a regression suite of 3,000 tests. They have trained a machine learning model to predict which tests are likely to fail on a given code change. The model has a precision of 80% and a recall of 60%. They plan to select tests with a predicted failure probability above a threshold. What is a likely consequence of using a high threshold?

   • AMore tests will be selected, increasing execution time
   • BFewer tests will be selected, but the selected tests are more likely to actually fail
   • CThe fault detection rate will always increase
   • DThe model will automatically retrain itself
   Reveal Solution Discussion Select an answer first
4. 23foundation · easy

   What is the main goal of AI-based test case selection in regression testing?

   • ATo choose a subset of tests that are most likely to detect faults
   • BTo prioritize tests based on execution time only
   • CTo execute all tests in a random order
   • DTo generate new test cases automatically
   Reveal Solution Discussion Select an answer first
5. 24application · medium

   A development team has a regression suite that runs in a CI pipeline. They want to run the most critical tests first so that if the pipeline is interrupted, the highest-risk tests have already executed. They have historical data on which modules have had the most defects and which tests cover those modules. Which AI-driven strategy should they use?

   • ARandomly shuffle the test order each run
   • BSelect only tests that cover new code changes
   • CPrioritize tests based on historical defect density of the covered modules
   • DRun tests in the order they were written
   Reveal Solution Discussion Select an answer first
6. 25expert · hard

   A team has a regression suite that runs in a CI pipeline. They want to prioritize tests so that the most critical tests run first, but they also need to ensure that the total execution time does not exceed a nightly window of 2 hours. They have historical failure data and code coverage data. Which approach best balances early fault detection and the time constraint?

   • APrioritize tests by a weighted score of risk and coverage, then stop after the 2-hour budget is reached
   • BPrioritize tests only by execution time, shortest first
   • CRandomly order tests and run as many as fit in 2 hours
   • DRun all tests in the suite, regardless of time
   Reveal Solution Discussion Select an answer first