一句话：批判性复审 NLC 覆盖准则，指出其违背单调性与测试套件顺序无关性，并揭示实证效度问题。
本文对 Yuan 等人在 ICSE 2023 提出的当前最优 DNN 覆盖准则 NLC 进行批判性复审。尽管 NLC 声称满足八项设计需求并具有良好实证表现，作者质疑其理论与实证假设。他们发现 NLC 偏离覆盖准则的核心原则，如单调性和测试套件顺序无关性，且对协方差矩阵关键性质的利用不充分；同时指出其实证研究在测试套件真值排序上存在效度威胁。通过实证验证，作者证实上述问题，并为未来 DNN 覆盖度量提出改进方向，最后讨论这些洞见对 DNN 覆盖测试的启示。
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.
@inproceedings{abs-2601-08729,
title = {Revisiting "Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion": A Critical Review and Implications on DNN Coverage Testing},
author = {Jinhan Kim and Nargiz Humbatova and Gunel Jahangirova and Shin Yoo and Paolo Tonella},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3744916.3773148}
}