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)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3643678

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