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09/14/2023
Pop Quiz! Do Pre-trained Code Models Possess Knowledge of Correct API Names?
Recent breakthroughs in pre-trained code models, such as CodeBERT and Co...
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08/28/2023
CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models
Code datasets are of immense value for training neural-network-based cod...
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05/31/2023
Data Augmentation Approaches for Source Code Models: A Survey
The increasingly popular adoption of source code in many critical tasks ...
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09/13/2022
Learning to Prevent Profitless Neural Code Completion
Currently, large pre-trained models are widely applied in neural code co...
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02/14/2022
On the Importance of Building High-quality Training Datasets for Neural Code Search
The performance of neural code search is significantly influenced by the...
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10/25/2021
CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning
Github Copilot, trained on billions of lines of public code, has recentl...
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04/24/2020
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Over the past decade, deep learning (DL) has been successfully applied t...
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11/03/2019
Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
Speaker recognition (SR) is widely used in our daily life as a biometric...
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09/08/2019
Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks
Vulnerability identification is crucial to protect the software systems ...
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01/31/2019
LEOPARD: Identifying Vulnerable Code for Vulnerability Assessment through Program Metrics
Identifying potentially vulnerable locations in a code base is critical ...
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12/13/2018