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VulScribeR: Exploring RAG-based Vulnerability Augmentation with LLMs
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/smr.2634openalex-snapshot
Mutation‐based data augmentation for software defect prediction
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ANTLR: A predicated‐ LL ( k ) parser generator
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Deep Learning-Based Vulnerable Function Detection: A Benchmark
inferredcites →https://doi.org/10.1007/978-3-031-55642-5openalex-snapshot
Generative AI for Effective Software Development
inferredcites →https://doi.org/10.1007/s10664-025-10624-2openalex-snapshot
Boosting source code learning with text-oriented data augmentation: an empirical study
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Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery
inferredcites →https://doi.org/10.1016/0004-3702(94)00094-8openalex-snapshot
Noise modelling and evaluating learning from examples
inferredcites →https://doi.org/10.1016/j.hcc.2024.100211openalex-snapshot
A survey on large language model (LLM) security and privacy: The Good, The Bad, and The Ugly
inferredcites →https://doi.org/10.1016/j.jss.2022.111304openalex-snapshot
Data Augmentation by Program Transformation
inferredcites →https://doi.org/10.1016/j.jss.2024.112031openalex-snapshot
GRACE: Empowering LLM-based software vulnerability detection with graph structure and in-context learning
inferredcites →https://doi.org/10.1109/access.2019.2917620openalex-snapshot
Diversity in Machine Learning
inferredcites →https://doi.org/10.1109/apr59189.2023.00012openalex-snapshot
An Analysis of the Automatic Bug Fixing Performance of ChatGPT
inferredcites →https://doi.org/10.1109/ase56229.2023.00109openalex-snapshot
What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?
inferredcites →https://doi.org/10.1109/icaica52286.2021.9497888openalex-snapshot
A Framework of Vulnerable Code Dataset Generation by Open-Source Injection
inferredcites →https://doi.org/10.1109/iccv.2019.00612openalex-snapshot
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
inferredcites →https://doi.org/10.1109/icse48619.2023.00022openalex-snapshot
Data Quality for Software Vulnerability Datasets
inferredcites →https://doi.org/10.1109/icse48619.2023.00189openalex-snapshot
DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection
inferredcites →https://doi.org/10.1109/icse48619.2023.00205openalex-snapshot
Retrieval-Based Prompt Selection for Code-Related Few-Shot Learning
inferredcites →https://doi.org/10.1109/icse48619.2023.00211openalex-snapshot
VULGEN: Realistic Vulnerability Generation Via Pattern Mining and Deep Learning
inferredcites →https://doi.org/10.1109/icsme58846.2023.00014openalex-snapshot
You Augment Me: Exploring ChatGPT-based Data Augmentation for Semantic Code Search
inferredcites →https://doi.org/10.1109/qrs-c60940.2023.00017openalex-snapshot
Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study
inferredcites →https://doi.org/10.1109/saner64311.2025.00053openalex-snapshot
Evaluating the Effectiveness and Efficiency of Demonstration Retrievers in RAG for Coding Tasks
inferredcites →https://doi.org/10.1109/tnnls.2013.2292894openalex-snapshot
Classification in the Presence of Label Noise: A Survey
inferredcites →https://doi.org/10.1109/tse.2019.2924371openalex-snapshot
Impact of Discretization Noise of the Dependent Variable on Machine Learning Classifiers in Software Engineering
inferredcites →https://doi.org/10.1109/tse.2021.3056941openalex-snapshot
The Impact of Feature Importance Methods on the Interpretation of Defect Classifiers
inferredcites →https://doi.org/10.1109/tse.2021.3087402openalex-snapshot
Deep Learning Based Vulnerability Detection: Are We There Yet?
inferredcites →https://doi.org/10.1109/wacv48630.2021.00141openalex-snapshot
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
inferredcites →https://doi.org/10.1145/3379597.3387501openalex-snapshot
A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
inferredcites →https://doi.org/10.1145/3404835.3462840openalex-snapshot
Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations
inferredcites →https://doi.org/10.1145/3468264.3468597openalex-snapshot
Vulnerability detection with fine-grained interpretations
inferredcites →https://doi.org/10.1145/3540250.3549162openalex-snapshot
NatGen: generative pre-training by “naturalizing” source code
inferredcites →https://doi.org/10.1145/3540250.3558931openalex-snapshot
MultIPAs: applying program transformations to introductory programming assignments for data augmentation
inferredcites →https://doi.org/10.1145/3587102.3588785openalex-snapshot
Comparing Code Explanations Created by Students and Large Language Models
inferredcites →https://doi.org/10.1145/3597503.3639085openalex-snapshot
Code Search is All You Need? Improving Code Suggestions with Code Search
inferredcites →https://doi.org/10.1145/3597503.3639116openalex-snapshot
VGX: Large-Scale Sample Generation for Boosting Learning-Based Software Vulnerability Analyses
inferredcites →https://doi.org/10.1145/3597503.3639150openalex-snapshot
LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing
inferredcites →https://doi.org/10.1145/3611643.3617850openalex-snapshot
LLM-Based Code Generation Method for Golang Compiler Testing
inferredcites →https://doi.org/10.1145/3652032.3657564openalex-snapshot
Enhancing Code Vulnerability Detection via Vulnerability-Preserving Data Augmentation
inferredcites →https://doi.org/10.1162/neco.1995.7.1.108openalex-snapshot
Training with Noise is Equivalent to Tikhonov Regularization
inferredcites →https://doi.org/10.1561/1500000019openalex-snapshot
The Probabilistic Relevance Framework: BM25 and Beyond
inferredcites →https://doi.org/10.18653/v1/2020.findings-emnlp.139openalex-snapshot
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
inferredcites →https://doi.org/10.18653/v1/2022.emnlp-main.327openalex-snapshot
Exploring Representation-level Augmentation for Code Search
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-981-95-5294-8_35openalex-snapshot
Source Code Vulnerability Detection Method Based on Hybrid Retrieval-Augmented Generation Technology
inferred← citeshttps://doi.org/10.1007/s10115-026-02859-2openalex-snapshot
Enhancing code smell classification with code refactoring-based data augmentation
inferred← citeshttps://doi.org/10.1016/j.sysarc.2026.103722openalex-snapshot
When fixes teach: Repair-aware contrastive learning for optimization-resilient binary vulnerability detection
inferred← citeshttps://doi.org/10.1109/access.2026.3663784openalex-snapshot
Transformation of Vulnerability Management Through Artificial Intelligence: An Overview of Generative and Learning Models
inferred← citeshttps://doi.org/10.1109/ainit70033.2026.11557646openalex-snapshot
Retrieval-Augmented Taint Specification Inference for Vulnerability Detection
inferred← citeshttps://doi.org/10.1109/compsac69091.2026.00046openalex-snapshot
From Stochastic Generation to Deterministic Logic: A Cross-Domain Survey of Prompt Engineering Across NLP, Vision, and Software Engineering
inferred← citeshttps://doi.org/10.1109/icsme64153.2025.00062openalex-snapshot
A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
inferred← citeshttps://doi.org/10.1109/mapr67746.2025.11133833openalex-snapshot
Multimodal Fusion for Vulnerability Detection: Integrating Sequence and Graph-Based Analysis with LLM Augmentation
inferred← citeshttps://doi.org/10.1109/saner-c67878.2026.00042openalex-snapshot
Persistent Human Feedback, LLMs, and Static Analyzers for Secure Code Generation and Vulnerability Detection
inferred← citeshttps://doi.org/10.1145/3744916.3773176openalex-snapshot
Exploring and Improving Real-World Vulnerability Data Generation via Prompting Large Language Models
inferred← citeshttps://doi.org/10.1145/3797277openalex-snapshot
vul-rag : Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
inferred← citeshttps://doi.org/10.1145/3808197openalex-snapshot
Three Heads Are Better Than One: A Multi-perspective Reasoning Framework for Enhanced Vulnerability Detection
inferred← citeshttps://doi.org/10.1145/3821416openalex-snapshot
Assessing and Improving Prompting Large Language Models for Software Vulnerability Analysis
inferred← citeshttps://doi.org/10.1145/3832193openalex-snapshot
Just Old Wine in a New Bottle: Improving Vulnerability Repair Robustness to Combat Homologous but Heterogeneous Vulnerabilities
inferred← citeshttps://doi.org/10.1145/3832783.3834522openalex-snapshot
No Isolated Evolution, No Blind Search: Memory-Guided Prompt Co-evolution for Industrial Multi-agent Vulnerability Detection
inferred← citeshttps://doi.org/10.1145/3832783.3837472openalex-snapshot
Fully Automating Template Polyhedral Analysis by Leveraging LLMs
inferred← citeshttps://doi.org/10.7717/peerj-cs.3847openalex-snapshot
Hybrid cache-graph retrieval-augmented generation (RAG): enhancing network threat analysis through structural reasoning and memory optimization
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