Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2023

Abstract

Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from defects that may lead to severe outcomes. DNN testing is one of the most widely used methods to ensure the quality of DNNs. Such method needs rich test inputs with oracle information (expected output) to reveal the incorrect behaviors of a DNN model. However, manually labeling all the collected test inputs is a labor-intensive task, which delays the quality assurance process. tackles this problem by carefully selecting a small, more suspicious set of test inputs to label, enabling the failure detection of a DNN model with reduced effort. Researchers have proposed different test selection methods, including neuron-coverage-based and uncertainty-based methods, where the uncertainty-based method is arguably the most popular technique. Unfortunately, existing uncertainty-based selection methods meet the performance bottleneck due to one or several limitations: 1) they ignore noisy data in real scenarios; 2) they wrongly exclude many but rather include many (referring to those test inputs that are correctly predicted by the model); 3) they ignore the diversity of the selected test set. In this paper, we propose RTS, a obust est election method for deep neural networks to overcome the limitations mentioned above. First, RTS divides all unlabeled candidate test inputs into noise set, successful set, and suspicious set and assigns different selection prioritization to divided sets, which effectively alleviates the impact of noise and improves the ability to identify suspect test inputs. Subsequently, RTS leverages a probability-tier-matrix-based test metric for prioritizing the test inputs in each divided set (i.e., suspicious, successful, and noise set). As a result, RTS can select more suspicious test inputs within a limited selection size. We evaluate RTS by comparing it with 14 baseline methods under 5 widely-used DNN models and 6 widely-used datasets. The experimental results demonstrate that RTS can significantly outperform all test selection methods in failure detection capability and the test suites selected by RTS have the best model optimization capability. For example, when selecting 2.5% test input, RTS achieves an improvement of 9.37%-176.75% over baseline methods in terms of failure detection.

Keywords

Artificial neural networks, Data models, Deep learning testing, deep neural networks, Labeling, Neurons, Noise measurement, Task analysis, test selection, Testing

Discipline

OS and Networks | Software Engineering | Theory and Algorithms

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Software Engineering

Volume

49

Issue

12

First Page

5250

Last Page

5278

ISSN

0098-5589

Identifier

10.1109/TSE.2023.3330982

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/0.1109/TSE.2023.3330982

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