For testing, which data-generation strategy is used to explore boundary conditions?

For testing, which data-generation strategy is used to explore boundary conditions?

• A. Mutation testing of synthetic data to expose edge cases
• B. Data minimization to reduce test scope
• C. Using only deterministic, bounded inputs
• D. Relying on random data with no structure

Answer: (A)

Boundary conditions are the points where behavior can change or fail, so you want data that pushes to the limits and tests robustness at those edges. Mutation testing of synthetic data does this by starting with controlled data and then making small, deliberate changes to create near-boundary scenarios, revealing whether the tests can detect faults under edge-case conditions. This focused, constructive alteration helps uncover gaps in test coverage where boundary behavior might go wrong. The other approaches either reduce coverage, overly restrict inputs, or rely on unstructured randomness, which is unlikely to consistently hit the boundary cases you need to evaluate.

Boundary conditions are the points where behavior can change or fail, so you want data that pushes to the limits and tests robustness at those edges. Mutation testing of synthetic data does this by starting with controlled data and then making small, deliberate changes to create near-boundary scenarios, revealing whether the tests can detect faults under edge-case conditions. This focused, constructive alteration helps uncover gaps in test coverage where boundary behavior might go wrong. The other approaches either reduce coverage, overly restrict inputs, or rely on unstructured randomness, which is unlikely to consistently hit the boundary cases you need to evaluate.