Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2021

Abstract

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.

Keywords

Measurement, Deep learning, Benchmark testing, Robustness, Robots, Software engineering, Convergence

Discipline

Numerical Analysis and Scientific Computing | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE): Proceedings: Virtual, May 22-30

First Page

300

Last Page

311

ISBN

9781665402965

Identifier

10.1109/ICSE43902.2021.00038

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

8-25-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICSE43902.2021.00038

Share

COinS