Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2022
Abstract
Existing approaches to improving the robustness of source code models concentrate on recognizing adversarial samples rather than valid samples that fall outside of a given distribution, which we refer to as out-of-distribution (OOD) samples. To this end, we propose to use an auxiliary dataset (out-of-distribution) such that, when trained together with the main dataset, they will enhance the model’s robustness. We adapt energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models. In terms of OOD detection and adversarial samples detection, our evaluation results demonstrate a greater robustness for existing source code models to become more accurate at recognizing OOD data while being more resistant to adversarial attacks at the same time.
Discipline
Software Engineering
Publication
ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, October 10-14
First Page
1
Last Page
3
ISBN
9781450396240
Identifier
10.1145/3551349.3561171
Publisher
ACM
City or Country
New York
Citation
BUI, Duy Quoc Nghi and YU, Yijun.
Towards robust models of code via energy-based learning on auxiliary datasets. (2022). ASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, Rochester, MI, October 10-14. 1-3.
Available at: https://ink.library.smu.edu.sg/sis_research/10117
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/10.1145/3551349.3561171
