The increasing deployment of deep learning systems requires systematic evaluation of their reliability in real-world scenarios. Traditional gradient-based adversarial attacks introduce small perturbations that rarely correspond to realistic failures and mainly assess robustness rather than functional behavior. Generative test generation methods offer an alternative but are often limited to simple datasets or constrained input domains. Although diffusion models enable high-fidelity image synthesis, their computational cost and limited controllability restrict their applicability to large-scale testing. We present HyNeA, a generative testing method that enables direct and efficient control over diffusion-based generation. HyNeA provides dataset-free controllability through hypernetworks, allowing targeted manipulation of the generative process without relying on architecture-specific conditioning mechanisms or dataset-driven adaptations such as fine-tuning. HyNeA employs a distinct training strategy that supports instance-level tuning to identify failure-inducing test cases without requiring datasets that explicitly contain examples of similar failures. This approach enables the targeted generation of realistic failure cases at substantially lower computational cost than search-based methods. Experimental results show that HyNeA improves controllability and test diversity compared to existing generative test generators and generalizes to domains where failure-labeled training data is unavailable.

翻译：深度学习系统日益广泛的应用要求对其在真实场景下的可靠性进行系统化评估。传统的基于梯度的对抗攻击方法引入微小扰动，这些扰动很少对应现实中的故障，且主要评估鲁棒性而非功能行为。生成式测试生成方法提供了替代方案，但通常局限于简单数据集或受限输入域。尽管扩散模型能够实现高保真图像合成，但其计算成本和有限的可控性限制了其在大规模测试中的应用。本文提出HyNeA，一种生成式测试方法，能够对基于扩散的生成过程实现直接高效的控制。HyNeA通过超网络提供无需数据集的操控能力，允许对生成过程进行定向调控，而无需依赖特定架构的条件机制或数据集驱动的适配方法（如微调）。HyNeA采用独特的训练策略，支持实例级调优以识别诱发故障的测试用例，且不需要包含类似故障示例的数据集。该方法能够以远低于基于搜索方法的计算成本，定向生成真实的故障案例。实验结果表明，与现有生成式测试生成器相比，HyNeA提升了可控性和测试多样性，并能泛化至缺乏故障标签训练数据的领域。