Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

Han Wang

doi:10.1145/3638245

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

2024-04-18

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DOI：10.1145/3638245

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Deep Learning (DL) models have rapidly advanced, focusing on achieving high performance through testing model accuracy and robustness. However, it is unclear whether DL projects, as software systems, are tested thoroughly or functionally correct when there is a need to treat and test them like other software systems. Therefore, we empirically study the unit tests in open-source DL projects, analyzing 9,129 projects from GitHub. We find that: (1) unit tested DL projects have positive correlationwith the open-source project metrics and have a higher acceptance rate of pull requests; (2) 68% of the sampled DL projects are not unit tested at all; (3) the layer and utilities (utils) of DL models have the most unit tests. Based on these findings and previous research outcomes, we built a mapping taxonomy between unit tests and faults in DL projects. We discuss the implications of our findings for developers and researchers and highlight the need for unit testing in opensource DL projects to ensure their reliability and stability. The study contributes to this community by raising awareness of the importance of unit testing in DL projects and encouraging further research in this area.

Keywords:

Deep learning

unit testing

Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects

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