If you are tracking the frequency that a test automation code reports a defect that is not really a defect, what metric are you gathering?
Options:
A.
Tool scripting metrics
B.
Automation code defect density
C.
Trend metrics
D.
The number of false-fail results
Answer:
D
Explanation:
Explanation:
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.
