A new data-driven programming model is defined by the deep learning (DL) that makes the internal structure of a created neuron system over a fixed of training data. DL testing structure only depends on the data labeling and manual group. Nowadays, a lot of coverage criteria have been developed, but these criteria basically count the neurons' quantity whose activation during the implementation of a DL structure fulfilled certain properties. Also, existing criteria are not adequately fine-grained to capture delicate behaviors. This paper develops an optimized deep belief network (DBN) with a search and rescue (SAR) algorithm for testing coverage criteria. For an optimal selection of DBN structure, the SAR algorithm is introduced. The main objective is to test the DL structure using different criteria to enhance the coverage accuracy. The different coverage criteria such as KMNC, NBC, SNAC, TKNC, and TKNP are used for the testing of DBN. Using the generated test inputs, the criteria is validated and the developed criteria are capable to capture undesired behaviors in the DBN structure. The developed approach is implemented by Python platform using three standard datasets like MNIST, CIFAR-10, and ImageNet. For analysis, the developed approach is compared with the three LeNet models like LeNet-1, LeNet-4 and LeNet-5 for the MNIST dataset, the VGG-16, and ResNet-20 models for the CIFAR-10 dataset, and the VGG-19 and ResNet-50 models for the ImageNet dataset. These models are tested on the four adversarial test input generation approaches like BIM, JSMA, FGSM, and CW, and one DL testing method like DeepGauge to validate the efficiency of the suggested approach. The simulation results proved that the proposed approach obtained high coverage accuracy for each criterion on four adversarial test inputs and one DL testing method as compared to other models.

深度学习（DL）定义了一种新的数据驱动的编程模型，该模型使固定的训练数据构成了所创建的神经元系统的内部结构。DL测试结构仅取决于数据标签和手册组。如今，已经开发了许多覆盖标准，但是这些标准基本上计算了神经元的数量，在执行DL结构的过程中神经元的激活满足了某些特性。此外，现有标准也无法充分细化以捕获微妙的行为。本文开发了一种具有搜索和救援（SAR）算法的优化深度信念网络（DBN），以测试覆盖范围标准。为了优化选择DBN结构，引入了SAR算法。主要目的是使用不同的标准测试DL结构，以提高覆盖范围的准确性。DBN的测试使用了不同的覆盖标准，例如KMNC，NBC，SNAC，TKNC和TKNP。使用生成的测试输入，可以验证标准，并且开发的标准能够捕获DBN结构中的不良行为。通过Python平台，使用MNIST，CIFAR-10和ImageNet等三个标准数据集实现了开发的方法。为了进行分析，将开发的方法与MNIST数据集的LeNet-1，LeNet-4和LeNet-5的三个LeNe​​t模型，CIFAR-10数据集的VGG-16和ResNet-20模型以及VGG进行了比较。 -19和ResNet-50模型用于ImageNet数据集。这些模型在四种对抗性测试输入生成方法（如BIM，JSMA，FGSM和CW）和一种DL测试方法（如DeepGauge）上进行了测试，以验证所建议方法的效率。