VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation | Paper Citation Record

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation , " * write output.state after.block = add.period write newline

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation write newline

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter

Reference 3

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 4

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This paper cites DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection.

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection

Reference 5

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 6

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 7

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Data Quality for Software Vulnerability Datasets

Reference 8

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This paper cites VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs.

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs

Reference 9

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 10

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Vulnerability Detection with Code Language Models: How Far Are We?

Reference 11

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 12

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 13

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation CodeBERT: A Pre-Trained Model for Programming and Natural Languages

Reference 14

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation GraphCodeBERT: Pre-training Code Representations with Data Flow

Reference 16

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 17

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 18

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 19

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 20

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Illuminati: Towards Explaining Graph Neural Networks for Cybersecurity Analysis

Reference 21

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Simple data balancing achieves competitive worst-group-accuracy

Reference 22

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Learning the Difference that Makes a Difference with Counterfactually-Augmented Data

Reference 23

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 25

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 26

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 27

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation

Reference 28

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation A Unified Approach to Interpreting Model Predictions

Reference 29

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation "Why Should I Trust You?": Explaining the Predictions of Any Classifier

Reference 33

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Explaining NLP Models via Minimal Contrastive Editing (MiCE)

Reference 34

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation C.; Hagenbuchner, M.; and Monfardini, G

Reference 35

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation K.; Mishra, J.; Singh, A.; Govil, R.; Srivastava, G.; and Lin, J

Reference 36

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation

Reference 37

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Code Vulnerability Detection: A Comparative Analysis of Emerging Large Language Models

Reference 38

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Solving the Class Imbalance Problem Using a Counterfactual Method for Data Augmentation

Reference 39

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This paper cites Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review.

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Reference 40

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks

Reference 41

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR

Reference 42

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 43

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 44

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Unresolved cited work

Reference 47

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Large Language Model for Vulnerability Detection: Emerging Results and Future Directions

Reference 48

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 49

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VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Security Vulnerability Detection Using Deep Learning Natural Language Processing

Reference 50

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