research-article

Adaptive test selection for deep neural networks

Authors:

Baowen XuAuthors Info & Claims

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

Pages 73 - 85

https://doi.org/10.1145/3510003.3510232

Published: 05 July 2022 Publication History

Abstract

Deep neural networks (DNN) have achieved tremendous development in the past decade. While many DNN-driven software applications have been deployed to solve various tasks, they could also produce incorrect behaviors and result in massive losses. To reveal the incorrect behaviors and improve the quality of DNN-driven applications, developers often need rich labeled data for the testing and optimization of DNN models. However, in practice, collecting diverse data from application scenarios and labeling them properly is often a highly expensive and time-consuming task.

In this paper, we proposed an adaptive test selection method, namely ATS, for deep neural networks to alleviate this problem. ATS leverages the difference between the model outputs to measure the behavior diversity of DNN test data. And it aims at selecting a subset with diverse tests from a massive unlabelled dataset. We experiment ATS with four well-designed DNN models and four widely-used datasets in comparison with various kinds of neuron coverage (NC). The results demonstrate that ATS can significantly outperform all test selection methods in assessing both fault detection and model improvement capability of test suites. It is promising to save the data labeling and model retraining costs for deep neural networks.

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Cited By

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https://dl.acm.org/doi/10.1145/3672451
Aghababaeyan ZAbdellatif MDadkhah MBriand L(2024)DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural NetworksACM Transactions on Software Engineering and Methodology10.1145/364438833:6(1-29)Online publication date: 27-Jun-2024
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Index Terms

Adaptive test selection for deep neural networks
1. 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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cover image ACM Conferences

ICSE '22: Proceedings of the 44th International Conference on Software Engineering

May 2022

2508 pages

ISBN:9781450392211

DOI:10.1145/3510003

General Chair:
Matthew B Dwyer
University of Virginia
,
Program Chairs:
Daniela Damian
University of Victoria, Canada
,
Andreas Zeller
CISPA, Germany

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Published: 05 July 2022

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National Natural Science Foundation of China
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ICSE '22: 44th International Conference on Software Engineering

May 21 - 29, 2022

Pennsylvania, Pittsburgh

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

Wan CLiu SXie SLiu YHoffmann HMaire MLu S(2024)Keeper: Automated Testing and Fixing of Machine Learning SoftwareACM Transactions on Software Engineering and Methodology10.1145/3672451Online publication date: 13-Jun-2024
https://dl.acm.org/doi/10.1145/3672451
Aghababaeyan ZAbdellatif MDadkhah MBriand L(2024)DeepGD: A Multi-Objective Black-Box Test Selection Approach for Deep Neural NetworksACM Transactions on Software Engineering and Methodology10.1145/364438833:6(1-29)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3644388
Hu QGuo YXie XCordy MMa LPapadakis MLe Traon Y(2024)Test Optimization in DNN Testing: A SurveyACM Transactions on Software Engineering and Methodology10.1145/364367833:4(1-42)Online publication date: 27-Jan-2024
https://dl.acm.org/doi/10.1145/3643678
Gao XWang ZFeng YMa LChen ZXu BRoychoudhury APaiva AAbreu RStorey M(2024)MultiTest: Physical-Aware Object Insertion for Testing Multi-sensor Fusion Perception SystemsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639191(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639191
Song DXie XSong JZhu DHuang YJuefei-Xu FMa L(2024)LUNA: A Model-Based Universal Analysis Framework for Large Language ModelsIEEE Transactions on Software Engineering10.1109/TSE.2024.3411928(1-28)Online publication date: 2024
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Li LTao CGuo HZhang JSun X(2024)FATS: Feature Distribution Analysis-Based Test Selection for Deep Learning EnhancementIEEE Transactions on Big Data10.1109/TBDATA.2023.333464810:2(132-145)Online publication date: Apr-2024
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Wang SLi DLi HZhao MWong W(2024)A Survey on Test Input Selection and Prioritization for Deep Neural Networks2024 10th International Symposium on System Security, Safety, and Reliability (ISSSR)10.1109/ISSSR61934.2024.00035(232-243)Online publication date: 16-Mar-2024
https://doi.org/10.1109/ISSSR61934.2024.00035
Yang FChen YChen TMa YLiao J(2024)DeepSense: test prioritization for neural network based on multiple mutation and manifold spatial distributionEvolutionary Intelligence10.1007/s12065-024-00961-4Online publication date: 30-Jul-2024
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Dang XLi YMa WGuo YHu QPapadakis MCordy MTraon Y(2024)Towards Exploring the Limitations of Test Selection Techniques on Graph Neural Networks: An Empirical StudyEmpirical Software Engineering10.1007/s10664-024-10515-y29:5Online publication date: 22-Jul-2024
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Hu QGuo YXie XCordy MPapadakis MLe Traon Y(2023)LaF: Labeling-free Model Selection for Automated Deep Neural Network ReusingACM Transactions on Software Engineering and Methodology10.1145/361166633:1(1-28)Online publication date: 31-Jul-2023
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