论文标题
测试神经网络的结构覆盖范围概述
An Overview of Structural Coverage Metrics for Testing Neural Networks
论文作者
论文摘要
深度神经网络（DNN）模型，包括在安全 - 关键领域中使用的模型，需要进行彻底测试，以确保它们在不同的情况下可以可靠地表现良好。在本文中，我们概述了测试DNN模型的结构覆盖范围，包括神经元覆盖范围（NC），K-Multisection神经元覆盖范围（KMNC），Top-K神经元覆盖范围（TKNC），神经元边界覆盖范围（NBC），强大的神经元激活（SNAC）（SNAC）（SNAC）和Modifiend/Dikistion/DCC（MC）和DC（MCC）（MC）。我们评估用于感知任务的现实DNN模型（包括LENET-1，LENET-4，LENET-5和RESNET20）以及自治（TAXINET）中使用的网络的指标。我们还提供了一个工具DNNCOV，可以测量所有这些指标的测试覆盖范围。 DNNCOV向研究人员和从业人员提供一份信息丰富的报道报告，以评估DNN测试的适当性，比较不同的覆盖范围，并在测试过程中更方便地检查模型的内部设备。
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage (NC), k-multisection neuron coverage (kMNC), top-k neuron coverage (TKNC), neuron boundary coverage (NBC), strong neuron activation coverage (SNAC) and modified condition/decision coverage (MC/DC). We evaluate the metrics on realistic DNN models used for perception tasks (including LeNet-1, LeNet-4, LeNet-5, and ResNet20) as well as on networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, compare different coverage measures, and to more conveniently inspect the model's internals during testing.
