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Streamlining deep learning test adequacy metrics into a single framework could drastically reduce the time researchers spend on tooling and configuration, enhancing reproducibility in the field.
Mure achieves a 44.54% reduction in mutation testing costs without sacrificing accuracy, challenging the trade-off between efficiency and mutation score fidelity.
