Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11): 339-348.doi: 10.11896/jsjkx.240900006

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Research on Optimization of Test Case Generation Based on Neuron Coverage Index

XIAO Ziqin, SHI Yaqing, QU Yubin   

  1. College of Command and Control Engineering,Army Engineering University,Nanjing 210007,China
  • Received:2024-09-02 Revised:2024-11-27 Online:2025-11-15 Published:2025-11-06
  • About author:XIAO Ziqi,born in 1999,postgraduate.Her main research interests include intelligent software testing and deep learning model testing.
    SHI Yaqing,born in 1981,Ph.D,professor,master's supervisor,is a member of CCF(No.49805M).Her main research interest is intelligent software testing.

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Abstract: DNNs have been widely applied in many fields,and testing them is particularly important due to their complexity and uncertainty.Traditional testing methods rely too much on a single indicator and cannot fully reveal the complete behavioral patterns of deep neural networks.Therefore,it is necessary to comprehensively consider different coverage indicators to more comprehensively evaluate the performance of the model.It combines six multi-granularity deep neural network coverage metrics,optimizes the mutation strategy and seed selection steps of fuzzy testing,generates high-quality and high-coverage test cases.Experi-ments are conducted on four models of different complexities on the MNIST and CIFAR10 datasets.The original training set and newly generated effective test cases are combined for retraining the model to classification accuracy.The experimental results show that this method can significantly improve coverage and classification accuracy by optimizing the model through adaptive retraining.

Key words: Neural networks, Image classification, Fuzzy testing, Mutation strategies, Test case generation

CLC Number: 

  • TP311.5
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