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    郭虹静, 陶传奇, 黄志球. 意外充分性引导的深度神经网络测试样本生成[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220745
    引用本文: 郭虹静, 陶传奇, 黄志球. 意外充分性引导的深度神经网络测试样本生成[J]. 计算机研究与发展. DOI: 10.7544/issn1000-1239.202220745
    Guo Hongjing, Tao Chuanqi, Huang Zhiqiu. Surprise Adequacy-Guided Deep Neural Network Test Inputs Generation[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220745
    Citation: Guo Hongjing, Tao Chuanqi, Huang Zhiqiu. Surprise Adequacy-Guided Deep Neural Network Test Inputs Generation[J]. Journal of Computer Research and Development. DOI: 10.7544/issn1000-1239.202220745

    意外充分性引导的深度神经网络测试样本生成

    Surprise Adequacy-Guided Deep Neural Network Test Inputs Generation

    • 摘要: 由于深度神经网络(deep neural network, DNN)模型的复杂性和不确定性等属性,对模型的一般行为和边界行为进行充分的测试是保障模型质量的重要手段. 当前的研究主要基于制定的覆盖准则,结合模糊测试技术生成衍生测试样本,从而提升测试充分性,但较少综合考虑测试样本的多样性及个体揭错能力. 意外充分性指标量化测试样本与训练集在神经元输出方面的差异,是测试充分性评估的重要指标,目前缺乏基于此指标的测试样本生成方法. 因此,提出了一种意外充分性引导的深度神经网络测试样本生成方法,首先,筛选对于决策结果贡献较大的重要神经元,以其输出值为特征,改进意外充分性指标;其次,基于测试样本的意外充分性度量筛选具有揭错能力的种子样本;最后,利用覆盖引导的模糊测试思想,将测试样本的意外充分性值和DNN模型预测的类别概率差异作为联合优化目标,利用梯度上升算法计算扰动,迭代生成测试样本. 为了验证所提方法的有效性,选取5个DNN模型作为被测对象,涵盖4种不同的图像数据集,实验结果表明,改进的意外充分性指标能够有效捕捉异常的测试样本,同时减少计算时间开销. 在测试样本生成方面,与DeepGini和RobOT方法相比,基于所提的种子样本选择策略生成的衍生测试集的意外覆盖率最高提升了5.9%和15.9%. 相比于DLFuzz和DeepXplore方法,所提方法的意外覆盖率最高提升了26.5%和33.7%.

       

      Abstract: Due to the complexity and uncertainty of DNN (deep neural network) models, generating test inputs to comprehensively test general and corner case behaviors of DNN models is of great significance for ensuring model quality. Current research primarily focuses on designing coverage criteria and utilizing fuzzing testing technique to generate test inputs, thereby improving test adequacy. However, few studies have taken into consideration the diversity and individual fault-revealing ability of test inputs. Surprise adequacy quantifies the neuron activation differences between a test input and the training set. It is an important metric to measure test adequacy, which has not been leveraged for test input generation. Therefore, this study proposes a surprise adequacy-guided test input generation approach. Firstly, it selects important neurons that contribute more to decision-making. Activation values of these neurons are used as features to improve the surprise adequacy metric. Then, seed test inputs are selected with error-revealing capability based on the improved surprise adequacy measurements. Finally, the approach utilizes the idea of Coverage-Guided Fuzzing Testing to jointly optimize the surprise adequacy value of test inputs and the prediction probability differences among classes. The gradient ascent algorithm is adopted to calculate the perturbation and iteratively generate test inputs. Empirical studies on 5 DNN models covering 4 different image datasets demonstrate that the improved surprise adequacy metric effectively captures surprising test inputs and reduces the time cost of the calculation. Concerning test input generation, compared with DeepGini and RobOT, the follow-up test set generated by using the proposed seed input selection strategy exhibits the highest surprise coverage improvement of 5.9% and 15.9%, respectively. Compared with DLFuzz and DeepXplore, the proposed approach achieves the highest surprise coverage improvement of 26.5% and 33.7%, respectively.

       

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