DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems
Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware and distributio...
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2023
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sg-smu-ink.sis_research-95192024-01-22T15:08:28Z DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems WANG, Longtian XIE, Xiaofei DU, Xiaoning TIAN, Meng GUO, Qing YANG, Zheng SHEN, Chao Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware and distribution-aware methods. However, distribution-unaware testing lacks effectiveness by not explicitly considering the distribution of test cases and may generate redundant errors (within same distribution). Distribution-aware testing techniques primarily focus on generating test cases that follow the training distribution, missing out-of-distribution data that may also be valid and should be considered in the testing process. In this paper, we propose a novel distribution-guided approach for generating valid test cases with diverse distributions, which can better evaluate the model's robustness (i.e., generating hard-to-detect errors) and enhance the model's robustness (i.e., enriching training data). Unlike existing testing techniques that optimize individual test cases, DistXplore optimizes test suites that represent specific distributions. To evaluate and enhance the model's robustness, we design two metrics: distribution difference, which maximizes the similarity in distribution between two different classes of data to generate hard-to-detect errors, and distribution diversity, which increase the distribution diversity of generated test cases for enhancing the model's robustness. To evaluate the effectiveness of DistXplore in model evaluation and enhancement, we compare DistXplore with 14 state-of-the-art baselines on 10 models across 4 datasets. The evaluation results show that DisXplore not only detects a larger number of errors (e.g., 2×+ on average). Furthermore, DistXplore achieves a higher improvement in empirical robustness (e.g., 5.2% more accuracy improvement than the baselines on average). 2023-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/8516 info:doi/10.1145/3611643.3616266 https://ink.library.smu.edu.sg/context/sis_research/article/9519/viewcontent/3611643.3616266.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Deep learning Neural networks Software defect analysis Software testing and debugging Artificial Intelligence and Robotics |
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Deep learning Neural networks Software defect analysis Software testing and debugging Artificial Intelligence and Robotics WANG, Longtian XIE, Xiaofei DU, Xiaoning TIAN, Meng GUO, Qing YANG, Zheng SHEN, Chao DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems |
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Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware and distribution-aware methods. However, distribution-unaware testing lacks effectiveness by not explicitly considering the distribution of test cases and may generate redundant errors (within same distribution). Distribution-aware testing techniques primarily focus on generating test cases that follow the training distribution, missing out-of-distribution data that may also be valid and should be considered in the testing process. In this paper, we propose a novel distribution-guided approach for generating valid test cases with diverse distributions, which can better evaluate the model's robustness (i.e., generating hard-to-detect errors) and enhance the model's robustness (i.e., enriching training data). Unlike existing testing techniques that optimize individual test cases, DistXplore optimizes test suites that represent specific distributions. To evaluate and enhance the model's robustness, we design two metrics: distribution difference, which maximizes the similarity in distribution between two different classes of data to generate hard-to-detect errors, and distribution diversity, which increase the distribution diversity of generated test cases for enhancing the model's robustness. To evaluate the effectiveness of DistXplore in model evaluation and enhancement, we compare DistXplore with 14 state-of-the-art baselines on 10 models across 4 datasets. The evaluation results show that DisXplore not only detects a larger number of errors (e.g., 2×+ on average). Furthermore, DistXplore achieves a higher improvement in empirical robustness (e.g., 5.2% more accuracy improvement than the baselines on average). |
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text |
author |
WANG, Longtian XIE, Xiaofei DU, Xiaoning TIAN, Meng GUO, Qing YANG, Zheng SHEN, Chao |
author_facet |
WANG, Longtian XIE, Xiaofei DU, Xiaoning TIAN, Meng GUO, Qing YANG, Zheng SHEN, Chao |
author_sort |
WANG, Longtian |
title |
DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems |
title_short |
DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems |
title_full |
DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems |
title_fullStr |
DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems |
title_full_unstemmed |
DistXplore: Distribution-guided testing for evaluating and enhancing deep learning systems |
title_sort |
distxplore: distribution-guided testing for evaluating and enhancing deep learning systems |
publisher |
Institutional Knowledge at Singapore Management University |
publishDate |
2023 |
url |
https://ink.library.smu.edu.sg/sis_research/8516 https://ink.library.smu.edu.sg/context/sis_research/article/9519/viewcontent/3611643.3616266.pdf |
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