Revisiting deep neural network test coverage from the test effectiveness perspective
نویسندگان
چکیده
Many test coverage metrics have been proposed to measure the Deep Neural Network (DNN) testing effectiveness, including structural and non-structural coverage. These are based on fundamental assumption: they correlated with effectiveness. However, assumption is still not validated sufficiently reasonably, which brings question usefulness of DNN This paper conducted a revisiting study existing from effectiveness perspective, effectively validate assumption. Here, we carefully considered diversity subjects, three criteria, both typical state-of-the-art metrics. Different all studies that deliver negative conclusions coverage, identified some positive their perspective. In particular, found complementary relationship between practical usage scenarios promising research directions for these
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ژورنال
عنوان ژورنال: Journal of software
سال: 2023
ISSN: ['1796-217X']
DOI: https://doi.org/10.1002/smr.2561