TESTNAV: Pareto-Guided Search for Compositional Robustness Testing

• URL: http://arxiv.org/abs/2608.19882v1
• Date: Thu, 20 Aug 2026 10:43:22 GMT
• Title: TESTNAV: Pareto-Guided Search for Compositional Robustness Testing
• Abstract summary: TESTNAV is a bi-objective robustness testing framework for deep learning models.<n>It maximises performance degradation while preserving input fidelity measured by modality-specific metrics.
• Score: 42.86570387250456
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning models remain vulnerable to real-world input perturbations, especially when multiple corruptions co-occur in the same input (e.g., brightness shifts and motion blur). Compositional testing reveals these interaction effects but introduces two challenges: combinatorial growth of the perturbation space as dimensions and severity levels increase, and uneven diagnostic value-many combinations yield unrealistically degraded inputs with limited practical relevance. We present TESTNAV, 1 a Pareto-guided robustness testing framework for efficiently exploring discrete, compositional perturbation spaces when only a limited number of perturbation configurations can be evaluated. TESTNAV prioritises severe yet realistic failures by formulating robustness testing as bi-objective optimisation: maximise performance degradation while preserving input fidelity measured by modality-specific metrics (e.g., SSIM and KID for vision; chrF and BERT-F1 for language and code). It uses NSGA-II to approximate the bi-objective Pareto front. Across four benchmarks spanning vision, natural language, and code generation, TESTNAV recovers Pareto fronts up to 2.15x faster than search-based baselines, using 35.8%-89.3% of the discrete perturbation space defined by four perturbation dimensions with six levels each.

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