Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2024
Abstract
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios remains an open challenge. To address this, we propose CureFuzz, a novel curiosity-driven black-box fuzz testing approach for SDMs. CureFuzz proposes a curiosity mechanism that allows a fuzzer to effectively explore novel and diverse scenarios, leading to improved detection of crash-triggering scenarios. Additionally, we introduce a multi-objective seed selection technique to balance the exploration of novel scenarios and the generation of crash-triggering scenarios, thereby optimizing the fuzzing process. We evaluate CureFuzz on various SDMs and experimental results demonstrate that CureFuzz outperforms the state-of-the-art method by a substantial margin in the total number of faults and distinct types of crash-triggering scenarios. We also demonstrate that the crash-triggering scenarios found by CureFuzz can repair SDMs, highlighting CureFuzz as a valuable tool for testing SDMs and optimizing their performance.
Keywords
Fuzz Testing, Sequential Decision Making, Deep Learning
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, Lisbon, Portugal, April 14-20
First Page
1
Last Page
14
ISBN
9798400702174
Identifier
10.1145/3597503.363914
Publisher
ACM
City or Country
New York
Citation
HE, Junda; YANG, Zhou; SHI, Jieke; YANG, Chengran; KIM, Kisub; XU, Bowen; ZHOU, Xin; and David LO.
Curiosity-driven testing for sequential decision-making process. (2024). ICSE '24: Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, Lisbon, Portugal, April 14-20. 1-14.
Available at: https://ink.library.smu.edu.sg/sis_research/9258
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional URL
https://doi.org/10.1145/3597503.363914