Class-adaptive oracle-free metamorphic test case prioritization framework for vision-based deep neural networks

Metamorphic testing (MT) provides an effective oracle-free strategy for evaluating deep neural networks under semantics-preserving perturbations. However, existing MT prioritization methods typically rely on static global heuristics and do not adequately capture class-dependent variation in model behavior or the complementary roles of predictive uncertainty and interpretability drift. This limitation is especially important in safety-critical vision applications. We propose a class-adaptive uncertainty-interpretability metamorphic test case prioritization framework (CUI-MTP) for vision-based deep learning systems. Rather than ranking individual samples, the framework prioritizes executable metamorphic test cases and formulates prioritization as a class-conditioned optimization problem that jointly models probabilistic instability and saliency-level behavioral change. Predictive uncertainty and interpretability drift are integrated through multi-objective Bayesian optimization with expected hypervolume improvement. Experiments on CIFAR-10, Fashion-MNIST, and ISIC2019 with ResNet-18, ResNet-50, and ConvNeXt-Base show that the proposed method consistently outperforms representative oracle-free baselines across datasets, architectures, and Top-N budgets, with statistically significant improvements ([Formula: see text]). Qualitative Grad-CAM case studies further show that the framework captures not only obvious prediction failures but also high-risk cases with substantial saliency drift. Overall, the results demonstrate a practical and scalable strategy for robust, safety-aware validation of vision-based deep learning systems.