TY - GEN
T1 - A Comprehensive Review on Deep Learning System Testing
AU - Li, Ying
AU - Shan, Chun
AU - Liu, Zhen
AU - Liao, Shuyan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems.
AB - Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems.
KW - Deep Learning System
KW - Deep Learning Testing
KW - Software Testing
UR - http://www.scopus.com/inward/record.url?scp=85218977160&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-1548-3_12
DO - 10.1007/978-981-96-1548-3_12
M3 - Conference contribution
AN - SCOPUS:85218977160
SN - 9789819615476
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 181
EP - 191
BT - Algorithms and Architectures for Parallel Processing - 24th International Conference, ICA3PP 2024, Proceedings
A2 - Zhu, Tianqing
A2 - Li, Jin
A2 - Castiglione, Aniello
PB - Springer Science and Business Media Deutschland GmbH
T2 - 24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024
Y2 - 29 October 2024 through 31 October 2024
ER -
