Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2024
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.
Keywords
Test optimization, DNN testing, low-labeling cost
Discipline
Databases and Information Systems | Software Engineering
Research Areas
Information Systems and Management
Areas of Excellence
Digital transformation
Publication
ACM Transactions on Software Engineering and Methodology
Volume
33
Issue
4
First Page
1
Last Page
42
ISSN
1049-331X
Identifier
10.1145/3643678
Publisher
Association for Computing Machinery (ACM)
Citation
HU, Qiang; GUO, Yuejun; XIE, Xiaofei; CORDY, Maxime; MA, Lei; PAPADAKIS, Mike; and LE TRAON, Yves.
Test optimization in DNN testing: A survey. (2024). ACM Transactions on Software Engineering and Methodology. 33, (4), 1-42.
Available at: https://ink.library.smu.edu.sg/sis_research/9094
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3643678