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An overview of structural coverage metrics for testing neural networks

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Abstract

Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios. In this article, we provide an overview of structural coverage metrics for testing DNN models, including neuron coverage, k-multisection neuron coverage, top-k neuron coverage, neuron boundary coverage, strong neuron activation coverage and modified condition/decision coverage. We evaluate the metrics on realistic DNN models used for perception tasks (LeNet-1, LeNet-4, LeNet-5, ResNet20) including networks used in autonomy (TaxiNet). We also provide a tool, DNNCov, which can measure the testing coverage for all these metrics. DNNCov outputs an informative coverage report to enable researchers and practitioners to assess the adequacy of DNN testing, to compare different coverage measures, and to more conveniently inspect the model’s internals during testing.

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Notes

  1. https://github.com/DNNCov/DNNCov

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Authors and Affiliations

  1. University of Texas at Austin, Austin, USA

    Muhammad Usman

  2. University of Manchester, Manchester, UK

    Youcheng Sun

  3. KBR Inc. at NASA Ames, Ames, USA

    Divya Gopinath

  4. Princeton University, Princeton, USA

    Rishi Dange

  5. Palo Alto High School, Palo Alto, USA

    Luca Manolache

  6. CMU Cylab and KBR Inc. at NASA Ames, Ames, USA

    Corina S. Păsăreanu

Authors
  1. Muhammad Usman
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  2. Youcheng Sun
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  3. Divya Gopinath
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  4. Rishi Dange
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  5. Luca Manolache
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  6. Corina S. Păsăreanu
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Corresponding author

Correspondence to Corina S. Păsăreanu.

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Usman, M., Sun, Y., Gopinath, D. et al. An overview of structural coverage metrics for testing neural networks. Int J Softw Tools Technol Transfer 25, 393–405 (2023). https://doi.org/10.1007/s10009-022-00683-x

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