Neuron semantic-guided test generation for deep neural networks fuzzing

• Main Authors: HUANG, Li, SUN, Weifeng, YAN, Meng, LIU, Zhongxin, LEI, Yan, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: Deep learning testing; fuzzing; test input generation; Software Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/10121; https://ink.library.smu.edu.sg/context/sis_research/article/11121/viewcontent/Neuron_Semantic_av.pdf
• Institution: Singapore Management University

In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based cri...