Test Optimization in DNN Testing: A Survey-Reference-Cited by-同舟云学术

Test Optimization in DNN Testing: A Survey

Published:2024-04-20 Issue:4 Volume:33 Page:1-42
ISSN:1049-331X
Container-title:ACM Transactions on Software Engineering and Methodology
language:en
Short-container-title:ACM Trans. Softw. Eng. Methodol.

Author:

Hu Qiang¹^ORCID,Guo Yuejun²^ORCID,Xie Xiaofei³^ORCID,Cordy Maxime⁴^ORCID,Ma Lei⁵^ORCID,Papadakis Mike¹^ORCID,Le Traon Yves¹^ORCID

Affiliation:

1. University of Luxembourg, Esch-sur-Alzette, Luxembourg

2. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

3. Singapore Management University, Singapore, Singapore

4. University of Luxembourg, Luxembourg, Luxembourg

5. The University of Tokyo, Edmonton, Japan and University of Albert, Canada

Abstract

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.

Funder

Luxembourg National Research Funds

European Union’s Horizon Research and Innovation Programme

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3643678

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