ISTQB CT-TAE Dumps Questions Answers

 The best internal metric to measure the quality of the Test Automation System (TAS) and the number of problems associated with it would be the defect density within the TAS automation code. This metric provides insight into the robustness and reliability of the automation code itself. A lower defect density indicates higher quality and fewer problems, while a higher defect density may signal the need for improvements in the automation code. This metric is internal because it pertains directly to the quality of the TAS rather than its impact on external processes. References = The ISTQB Test Automation Engineer syllabus and other ISTQB materials emphasize the importance of internal metrics such as defect density to assess the quality and effectiveness of the TAS123.

Tracking the frequency of test automation code reporting defects that are not actual defects in the System Under Test (SUT) is known as measuring the number of false-fail results (Option D). This metric is crucial in evaluating the precision and reliability of the Test Automation Solution (TAS). False-fail results can lead to unnecessary investigation work, reducing the efficiency of the testing process and potentially eroding trust in the automation. By monitoring and minimizing false-fails, a Test Automation Engineer (TAE) can enhance the TAS's effectiveness, ensuring that it accurately reflects the state of the SUT and provides valuable feedback to the development team. This metric directly impacts the credibility and utility of automated testing by indicating how often the automation incorrectly flags a pass condition as a fail, thereby necessitating refinement of the automation code or test environment configurations to reduce these occurrences.

When faced with an intermittently failing test, it is essential to remove it from the test suite to prevent it from affecting the overall test results and reliability. The next step is to conduct a thorough investigation to understand why the test is producing inconsistent results. This could involve examining the test environment, the test data, the timing of the test execution, and any potential race conditions or dependencies that could cause the test to behave differently from one run to another. Understanding and resolving the root cause of the intermittent failure is crucial to ensure the stability and accuracy of the automated test suite.

References = The approach to handling intermittently failing tests is discussed in various ISTQB Test Automation Engineer documents, which emphasize the importance of maintaining the integrity of the test suite and ensuring consistent and reliable test results123.