In which ONE of the following situations would an ML model be MOST effective at determining the criticality of new defects?

Question

Options

• AA new application which is in the early stages of the first test cycle
• BAn old application with lots of defect records but a brand new development and test team
• CAn old application where defect records are linked to failed tests and production incidents
• DAn old application with few critical defect records and many non-critical defect records

• A
  15% (3)
• B
  5% (1)
• C
  75% (15)
• D
  5% (1)

Explanation

An old application where defect records are linked to failed tests and production incidents would provide the most valuable data for an ML model to determine the criticality of new defects. By using historical data of defects that are linked to actual issues in production or testing failures, the model can learn patterns and correlations between defects and their criticality, making it highly effective in predicting the criticality of new defects. This type of historical data provides the necessary context for accurate predictions.

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