Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

• URL: http://arxiv.org/abs/2606.04314v1
• Date: Wed, 03 Jun 2026 00:42:10 GMT
• Title: Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes
• Abstract summary: BayesWarp is a testing framework that mutates decision-critical input regions and adaptively guides the testing process.<n>We show that BayesWarp improves failure discovery, failure diversity, test case quality, and critical neuron coverage under a fixed mutation budget.
• Score: 9.231653207554325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability. Existing testing methods, whether black-box or white-box, primarily use global mutation or coverage-guided strategies, both of which struggle to efficiently uncover diverse model failures while remaining proximate to the original data distribution and semantics. We propose BayesWarp, a testing framework that addresses this limitation by mutating decision-critical input regions identified via interpretable saliency techniques and adaptively guiding the testing process using an uncertainty-aware Bayesian Optimization strategy, enabling the discovery of diverse failures while preserving distributional and semantic proximity to the original data. Evaluation on MNIST, CIFAR-10, and ImageNet across six neural network models shows that BayesWarp improves failure discovery, failure diversity, test case quality, and critical neuron coverage under a fixed mutation budget. These results demonstrate that BayesWarp improves testing effectiveness. Moreover, fine-tuning with the generated failure cases leads to improvements in model performance.

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