Deep neural network (DNN) testing is crucial for the reliability and safety of critical systems, where failures can have severe consequences. Although various techniques have been developed to create robustness test suites, requirements-based testing for DNNs remains largely unexplored - yet such tests are recognized as an essential component of software validation of critical systems. In this work, we propose a requirements-based test suite generation method that uses structured natural language requirements formulated in a semantic feature space to create test suites by prompting text-conditional latent diffusion models with the requirement precondition and then using the associated postcondition to define a test oracle to judge outputs of the DNN under test. We investigate the approach using fine-tuned variants of pre-trained generative models. Our experiments on the MNIST, CelebA-HQ, ImageNet, and autonomous car driving datasets demonstrate that the generated test suites are realistic, diverse, consistent with preconditions, and capable of revealing faults.

本研究解决了深度神经网络（DNN）测试中的需求基础测试不足的问题，提出了一种基于结构化自然语言需求的测试用例生成方法。通过使用文本条件潜在扩散模型和定义测试oracle，该方法生成的测试套件被验证为具有现实性、多样性和一致性，有助于发现DNN中的缺陷，从而提升关键系统的可靠性和安全性。

基于需求的神经网络测试：RBT4DNN