CT-AI practice question 3 of 15

Which ONE of the following is the BEST option to optimize the regression test selection and prevent the regression suite from growing large? SELECT ONE OPTION

Full set: 120 questions

Answer options

A: Identifying suitable tests by looking at the complexity of the test cases.

B: Using of a random subset of tests.

C: Automating test scripts using Al-based test automation tools.

D: Using an Al-based tool to optimize the regression test suite by analyzing past test results Correct answer

Correct Answer:

D. Using an Al-based tool to optimize the regression test suite by analyzing past test results

Explanation

The goal is to optimize regression test selection to manage the suite's size, meaning intelligently choosing a relevant subset of tests to run. An AI-based tool can analyze historical data, such as past test outcomes, code changes, and code coverage, to predict which existing test cases are most likely to detect new faults. This predictive capability allows for the creation of a smaller, more effective regression suite that maximizes fault detection while minimizing execution time and maintenance effort. This directly addresses the core problem of optimizing the selection process.

Why Incorrect

A. Test case complexity is not a reliable predictor of its relevance to a specific code change or its ability to find a regression bug.
B. A random subset is not an optimized approach; it provides no guarantee of covering the areas of the application affected by recent changes.
C. Automating test scripts improves execution efficiency but does not help in selecting which tests to run; it does not solve the problem of suite size.

References

1. ISTQB® (2021). Certified Tester AI Testing (CT-AI) Syllabus Version 1.0. Section 5.2.2, p. 47. (Describes "Test suite optimization" where AI can be used to "select a subset of test cases from an existing test suite for regression testing").

2. Marijan, D., & Gotlieb, A. (2020). Software Testing and AI. Chapter 10: "AI for Regression Testing," pp. 221-244. (Discusses how machine learning models can be trained on historical test data to prioritize and select test cases for regression). DOI: https://doi.org/10.1007/978-3-030-52583-910

3. El-Sayed, N., & Hemalatha, M. (2021). A Survey on AI-Based Test Case Prioritization. International Journal of Advanced Computer Science and Applications, 12(1). (Surveys various ML techniques used to analyze test history and code changes for optimizing regression test suites). DOI: http://dx.doi.org/10.14569/IJACSA.2021.0120168

Keep practicing

Take the free CT-AI practice test

Ten exam-style questions with answers and explanations, plus the exam facts and study guides.

Start the free practice test