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New framework improves deep learning testing by selecting high-quality mutants

By PulseAugur Editorial · [3 sources] ·

Researchers have developed a new probabilistic framework to assess the quality of mutants used in deep learning testing. This framework quantifies mutant quality based on resistance and realism, addressing a gap in current DL literature. The approach allows for the ranking and filtering of low-quality mutation-operator configurations, potentially reducing the number of generated mutants by over 55% while maintaining their effectiveness for testing and debugging. AI

IMPACT Introduces a new method to improve the efficiency and effectiveness of deep learning testing and debugging.

RANK_REASON This is a research paper introducing a new framework for deep learning testing.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New framework improves deep learning testing by selecting high-quality mutants

COVERAGE [3]

  1. arXiv cs.LG TIER_1 English(EN) · Zaheed Ahmed, Emmanuel Charleson Dapaah, Philip Makedonski, Jens Grabowski ·

    Quality-Driven Selective Mutation for Deep Learning

    arXiv:2604.22640v1 Announce Type: cross Abstract: Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, while realism is essential when mutants are used to …

  2. arXiv cs.LG TIER_1 English(EN) · Jens Grabowski ·

    Quality-Driven Selective Mutation for Deep Learning

    Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, while realism is essential when mutants are used to simulate real bugs. Building on these roles, selec…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    Quality-Driven Selective Mutation for Deep Learning

    Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better guidance for test improvement, while realism is essential when mutants are used to simulate real bugs. Building on these roles, selec…

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