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Deep Learning System and It’s Automatic Testing: An Approach

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  • Rijwan Khan

    (ABES Institute of Technology)

Abstract

The process of testing conventional programs is quite easy as compared to the programs using Deep Learning approach. The term Deep learning (DL) is used for a novel programming approach that is highly data centric and where the governing rules and logic are primarily dependent on the data used for training. Conventionally, Deep Learning models are evaluated by using a test dataset to evaluate their performance against set parameters. The difference in data and logic handling between programs using conventional methods and programs using the DL approach makes it difficult to apply the traditional approaches of testing directly to DL based programs. The accuracy of test data is currently the best measure of the adequacy of testing in the DL based systems. This poses a problem because of the difficulty in availability of test data that is of sufficient quality. This in turn restricts the level of confidence that can be established on the adequacy of testing of DL based systems. Unlike conventional applications, using the conventional programming approaches the lack of quality test data and the lack of interpretability makes the system analysis and detection of defects a difficult task in DL based systems. So testing of DL based models can be done automatically with a different approach compared to normal software.

Suggested Citation

  • Rijwan Khan, 2023. "Deep Learning System and It’s Automatic Testing: An Approach," Annals of Data Science, Springer, vol. 10(4), pages 1019-1033, August.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00361-w
    DOI: 10.1007/s40745-021-00361-w
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    References listed on IDEAS

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    1. Hossein Hassani & Xu Huang & Emmanuel Silva & Mansi Ghodsi, 2020. "Deep Learning and Implementations in Banking," Annals of Data Science, Springer, vol. 7(3), pages 433-446, September.
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    3. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
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