Which statement regarding AI for defect prediction is correct?

The correct answer is A. AI-based defect prediction is most effective when based on previous similar constellations. Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can…

Testing of AI-based Systems

Question

Options

AAI-based defect prediction is most effective when based on previous similar constellations.
BAI-based defect prediction can detect whether defects exist but not where.
CAI-based defect prediction is most effective when based on source-code metrics such as
DAI-based defect prediction is based on formal principles and requires only a few factors.

How the community answered

(31 responses)

A
77% (24)
B
13% (4)
C
6% (2)
D
3% (1)

Explanation

Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.

Topics

#AI defect prediction#ML-based testing#source code metrics#defect prediction

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