Test optimization in DNN testing: A survey

This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) communi...

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Main Authors: HU, Qiang, GUO, Yuejun, XIE, Xiaofei, CORDY, Maxime, MA, Lei, PAPADAKIS, Mike, LE TRAON, Yves
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9094
https://ink.library.smu.edu.sg/context/sis_research/article/10097/viewcontent/3643678.pdf
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spelling sg-smu-ink.sis_research-100972024-08-01T15:09:08Z Test optimization in DNN testing: A survey HU, Qiang GUO, Yuejun XIE, Xiaofei CORDY, Maxime MA, Lei PAPADAKIS, Mike LE TRAON, Yves This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain. 2024-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9094 info:doi/10.1145/3643678 https://ink.library.smu.edu.sg/context/sis_research/article/10097/viewcontent/3643678.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 Test optimization DNN testing low-labeling cost Databases and Information Systems Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Test optimization
DNN testing
low-labeling cost
Databases and Information Systems
Software Engineering
spellingShingle Test optimization
DNN testing
low-labeling cost
Databases and Information Systems
Software Engineering
HU, Qiang
GUO, Yuejun
XIE, Xiaofei
CORDY, Maxime
MA, Lei
PAPADAKIS, Mike
LE TRAON, Yves
Test optimization in DNN testing: A survey
description This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.
format text
author HU, Qiang
GUO, Yuejun
XIE, Xiaofei
CORDY, Maxime
MA, Lei
PAPADAKIS, Mike
LE TRAON, Yves
author_facet HU, Qiang
GUO, Yuejun
XIE, Xiaofei
CORDY, Maxime
MA, Lei
PAPADAKIS, Mike
LE TRAON, Yves
author_sort HU, Qiang
title Test optimization in DNN testing: A survey
title_short Test optimization in DNN testing: A survey
title_full Test optimization in DNN testing: A survey
title_fullStr Test optimization in DNN testing: A survey
title_full_unstemmed Test optimization in DNN testing: A survey
title_sort test optimization in dnn testing: a survey
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9094
https://ink.library.smu.edu.sg/context/sis_research/article/10097/viewcontent/3643678.pdf
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