Towards Robust Models of Code via Energy-Based Learning on Auxiliary Datasets
Authors: 
Nghi D. Q. Bui, 
Yijun YuAuthors Info & Claims
Nghi D. Q. Bui, Yijun YuAuthors Info & ClaimsASE '22: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Article No.: 147, Pages 1 - 3
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.
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Published In
October 2022
2006 pages
ISBN:9781450394758
DOI:10.1145/3551349
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Published: 05 January 2023
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ASE '22
ASE '22: 37th IEEE/ACM International Conference on Automated Software Engineering
October 10 - 14, 2022
MI, Rochester, USA
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Overall Acceptance Rate 82 of 337 submissions, 24%
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