RobOT: Robustness-Oriented Testing for Deep Learning Systems

Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided...

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Vydáno v:Proceedings / International Conference on Software Engineering s. 300 - 311
Hlavní autoři: Wang, Jingyi, Chen, Jialuo, Sun, Youcheng, Ma, Xingjun, Wang, Dongxia, Sun, Jun, Cheng, Peng
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.05.2021
Témata:
Benchmark testing
Convergence
Deep learning
Measurement
Robots
Robustness
Software engineering
ISBN:1665402962, 9781665402965
ISSN:1558-1225
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Shrnutí:Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a.~bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by proposing a novel testing framework called Robustness-Oriented Testing (RobOT). A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement. RobOT utilizes the proposed metric to automatically generate test cases valuable for improving model robustness. The proposed metric is also a strong indicator on how well robustness improvement has converged through testing. Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.
ISBN:1665402962
9781665402965
ISSN:1558-1225
DOI:10.1109/ICSE43902.2021.00038