research-article

QuoTe: Quality-oriented Testing for Deep Learning Systems

Authors:

Peng ChengAuthors Info & Claims

ACM Transactions on Software Engineering and Methodology, Volume 32, Issue 5

Article No.: 125, Pages 1 - 33

https://doi.org/10.1145/3582573

Published: 22 July 2023 Publication History

Abstract

Recently, there has been significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is DL testing—that is, given a property of test, defects 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 neuron coverage metrics, which are commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model quality after testing. In this work, we address this gap by proposing a novel testing framework called QuoTe (i.e., Quality-oriented Testing). A key part of QuoTe is a quantitative measurement on (1) the value of each test case in enhancing the model property of interest (often via retraining) and (2) the convergence quality of the model property improvement. QuoTe utilizes the proposed metric to automatically select or generate valuable test cases for improving model quality. The proposed metric is also a lightweight yet strong indicator of how well the improvement converged. Extensive experiments on both image and tabular datasets with a variety of model architectures confirm the effectiveness and efficiency of QuoTe in improving DL model quality—that is, robustness and fairness. As a generic quality-oriented testing framework, future adaptations can be made to other domains (e.g., text) as well as other model properties.

Appendix

A Additional Figures

Figure A.1.

Figure A.1. The FOL distribution of FGSM and PGD attacks for different models.

Figure A.2.

Figure A.2. The FOL distribution of AEQUITAS and ADF testing algorithms for different models.

Figure A.3.

Figure A.3. The trend of robustness improvement (ATTACK) in iterations on the MNIST and CIFAR-10 datasets (with confidence interval of 95% significance level).

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Index Terms

QuoTe: Quality-oriented Testing for Deep Learning Systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

cover image ACM Transactions on Software Engineering and Methodology

ACM Transactions on Software Engineering and Methodology Volume 32, Issue 5

September 2023

905 pages

ISSN:1049-331X

EISSN:1557-7392

DOI:10.1145/3610417

Editor:
Mauro Pezzè
USI Università della Svizzera italiana and SIT Schaffhausen Institute of Technology, Switzerland

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Publication History

Published: 22 July 2023

Online AM: 10 February 2023

Accepted: 16 December 2022

Revised: 04 December 2022

Received: 02 March 2022

Published in TOSEM Volume 32, Issue 5

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National Key R&D Program of China
Key R&D Program of Zhejiang
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Fundamental Research Funds for Central Universities

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https://dl.acm.org/doi/10.3233/JIFS-232700
Du CChen T(2024)Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning SystemsProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3686673(107-118)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3674805.3686673
Tambon FKhomh FAntoniol G(2024)GIST: Generated Inputs Sets Transferability in Deep LearningACM Transactions on Software Engineering and Methodology10.1145/3672457Online publication date: 13-Jun-2024
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