Uncertainty and Geometric Dispersion-Driven Metamorphic Testing for DNN-Based Systems

Metamorphic testing (MT) has emerged as a widely adopted technique for validating deep learning (DL) models in the absence of explicit test oracles. A key challenge in MT is the efficient selection of metamorphic groups (MGs), i.e. the source and follow-up test inputs, that are more likely to expose faults. To address this challenge, we propose UGD, a novel MT approach that integrates two complementary criteria, input uncertainty and geometric dispersion. In UGD, source inputs with high uncertainty are prioritized for testing, as these inputs are more likely to lie near decision boundaries and thereby reveal erroneous behaviors. For each selected source, a convex hull-based strategy is applied to choose follow-up inputs that are both distant from the source input and well-dispersed from each other. This design ensures that the generated MGs are diverse and fault-revealing. Extensive experiments demonstrate that UGD consistently outperforms existing baseline methods in terms of the number of violated MGs and unique faults detected, particularly on complex datasets such as ImageNet under limited test budgets. The results confirm that uncertainty is a reliable indicator for selecting fault-prone source inputs. Furthermore, geometric dispersion, guided by convex hulls, enhances fault detection by ensuring that follow-up inputs are sufficiently different from the source and diverse among themselves.