Using Metamorphic Testing to Evaluate DNN Coverage Criteria
Jinyi Zhou, Kun Qiu, Zheng Zheng, Tsong Yueh Chen, Pak‐Lok Poon · 2020
Generating test cases and further evaluating their “quality” are two critical topics in the area of Deep Neural Networks (DNNs). In this domain, different studies (e.g., [1], [2]) have reported that metamorphic testing (MT) serves as an effective test case generation method, where an initial set of source test cases is augmented with identified metamorphic relations (MRs) to produce the corresponding set of follow-up test cases. As a result, the fault detection effectiveness (and, hence, the “quality”) of the resulting test suite T, containing these source and follow-up test cases, will most likely be increased.