We present a critical review of Neural Coverage (NLC), a state-of-the-art DNN coverage criterion by Yuan et al. at ICSE 2023. While NLC proposes to satisfy eight design requirements and demonstrates strong empirical performance, we question some of their theoretical and empirical assumptions. We observe that NLC deviates from core principles of coverage criteria, such as monotonicity and test suite order independence, and could more fully account for key properties of the covariance matrix. Additionally, we note threats to the validity of the empirical study, related to the ground truth ordering of test suites. Through our empirical validation, we substantiate our claims and propose improvements for future DNN coverage metrics. Finally, we conclude by discussing the implications of these insights.

翻译：我们对Yuan等人在ICSE 2023上提出的最先进的深度神经网络覆盖率准则——神经元覆盖率（NLC）——进行了批判性回顾。尽管NLC旨在满足八项设计要求并展现出强大的实证性能，但我们对其部分理论与实证假设提出了质疑。我们观察到，NLC偏离了覆盖率准则的核心原则，如单调性和测试套件顺序独立性，并且未能充分考虑协方差矩阵的关键属性。此外，我们指出了其实证研究中存在的有效性威胁，涉及测试套件的真实排序问题。通过我们的实证验证，我们证实了上述观点，并为未来的深度神经网络覆盖率度量提出了改进建议。最后，我们讨论了这些见解的启示意义。