{"ID":3053301,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T00:31:28.115859392Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04314","arxiv_id":"2606.04314","title":"Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes","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.","short_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 fail...","url_abs":"https://arxiv.org/abs/2606.04314","url_pdf":"https://arxiv.org/pdf/2606.04314v1","authors":"[\"Bin Duan\",\"Meiru Che\",\"Guowei Yang\"]","published":"2026-06-03T00:42:10Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.SE\"]","methods":"[\"LoRA\"]","has_code":false}