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        <identifier>oai:figshare.com:article/33965359</identifier>
        <datestamp>2026-09-26T09:43:02Z</datestamp>
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          <dc:title>Data and Reproducibility Materials for HMF-PC-MTP: Multi-Fidelity Pareto Prioritization for Deep Neural Network Testing</dc:title>
          <dc:creator>JUNHAN LI (25088176)</dc:creator>
          <dc:creator>Radziah Mohamad (25090373)</dc:creator>
          <dc:creator>Johanna Ahmad (25090375)</dc:creator>
          <dc:subject>Software engineering not elsewhere classified</dc:subject>
          <dc:subject>Artificial intelligence not elsewhere classified</dc:subject>
          <dc:subject>Machine learning not elsewhere classified</dc:subject>
          <dc:subject>metamorphic testing</dc:subject>
          <dc:subject>deep neural networks</dc:subject>
          <dc:subject>test case prioritization</dc:subject>
          <dc:subject>deep learning testing</dc:subject>
          <dc:subject>multi-fidelity optimization</dc:subject>
          <dc:subject>multi-objective optimization</dc:subject>
          <dc:subject>Bayesian optimization</dc:subject>
          <dc:subject>uncertainty</dc:subject>
          <dc:subject>interpretability</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;&lt;b&gt;This dataset provides the experimental data, analysis outputs, and reproducibility materials supporting the study on HMF-PC-MTP, a multi-fidelity Pareto-based metamorphic test prioritization approach for deep neural network testing. The experiments cover CIFAR-10 and ISIC 2019 using ResNet-50 and ConvNeXt-Base under multiple class-balanced Top-K budgets and repeated experimental seeds.&lt;/b&gt;&lt;/p&gt;&lt;p dir="ltr"&gt;&lt;b&gt;The CIFAR-10 experiments use the standard 50,000-image training set. The revised ISIC 2019 experiments use the complete official 25,331-image training pool under an eight-class closed-set protocol. A separate held-out known-class test set of 6,191 images is used only for checkpoint generalization auditing and is not included in the primary HMF-PC-MTP prioritization statistics.&lt;/b&gt;&lt;/p&gt;&lt;p dir="ltr"&gt;&lt;b&gt;The deposited materials support the main quantitative analyses, external baseline comparisons, ablation studies, robustness analyses, efficiency evaluation, and metamorphic-relation allocation analyses reported in the manuscript. Four label-preserving metamorphic relations are evaluated across class-balanced Top-K settings and repeated seeds.&lt;/b&gt;&lt;/p&gt;&lt;p dir="ltr"&gt;&lt;b&gt;Please refer to the README included in the archive for the directory structure, file descriptions, experimental settings, and reproduction instructions.&lt;/b&gt;&lt;/p&gt;</dc:description>
          <dc:date>2026-09-26T09:43:02Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.6084/m9.figshare.33965359.v2</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Data_and_Reproducibility_Materials_for_HMF-PC-MTP_Multi-Fidelity_Pareto_Prioritization_for_Deep_Neural_Network_Testing/33965359</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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