{"total":103,"rows":[{"id":"2103.06333","title":"Unified Pre-training for Program Understanding and Generation","created":"2021-03-10","cited_by_count":366,"area":null},{"id":"2304.00409","title":"DiverseVul: A New Vulnerable Source Code Dataset for Deep Learning Based Vulnerability Detection","created":"2023-04-01","cited_by_count":102,"area":null},{"id":"2107.08760","title":"CVEfixes: Automated Collection of Vulnerabilities and Their Fixes from Open-Source Software","created":"2021-07-19","cited_by_count":91,"area":null},{"id":"2205.12424","title":"VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection","created":"2022-05-25","cited_by_count":51,"area":null},{"id":"2102.07995","title":"D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis","created":"2021-02-16","cited_by_count":49,"area":null},{"id":"2303.03004","title":"xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval","created":"2023-03-06","cited_by_count":47,"area":null},{"id":"2212.08109","title":"An Empirical Study of Deep Learning Models for Vulnerability Detection","created":"2022-12-15","cited_by_count":46,"area":null},{"id":"2204.03214","title":"Transformer-Based Language Models for Software Vulnerability Detection","created":"2022-04-07","cited_by_count":39,"area":null},{"id":"2104.08308","title":"Neural Transfer Learning for Repairing Security Vulnerabilities in C Code","created":"2021-04-16","cited_by_count":36,"area":null},{"id":"2307.02192","title":"The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification","created":"2023-07-05","cited_by_count":34,"area":null},{"id":"2110.07317","title":"ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection","created":"2021-10-14","cited_by_count":32,"area":null},{"id":"2307.02503","title":"Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review","created":"2023-07-04","cited_by_count":32,"area":null},{"id":"2111.05711","title":"Counterfactual Explanations for Models of Code","created":"2021-11-10","cited_by_count":25,"area":null},{"id":"2110.09610","title":"A Survey on Machine Learning Techniques for Source Code Analysis","created":"2021-10-18","cited_by_count":24,"area":null},{"id":"2112.10893","title":"VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements","created":"2021-12-20","cited_by_count":20,"area":null},{"id":"2208.10605","title":"SoK: Explainable Machine Learning for Computer Security Applications","created":"2022-08-22","cited_by_count":20,"area":null},{"id":"2212.08108","title":"Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection","created":"2022-12-15","cited_by_count":19,"area":null},{"id":"2107.08364","title":"A Survey on Data-driven Software Vulnerability Assessment and Prioritization","created":"2021-07-18","cited_by_count":18,"area":null},{"id":"2201.08441","title":"VUDENC: Vulnerability Detection with Deep Learning on a Natural Codebase for Python","created":"2022-01-20","cited_by_count":17,"area":null},{"id":"2306.01754","title":"Transformer-based Vulnerability Detection in Code at EditTime: Zero-shot, Few-shot, or Fine-tuning?","created":"2023-05-23","cited_by_count":13,"area":null},{"id":"2105.02388","title":"Security Vulnerability Detection Using Deep Learning Natural Language Processing","created":"2021-05-06","cited_by_count":12,"area":null},{"id":"2203.09907","title":"Learning to Reduce False Positives in Analytic Bug Detectors","created":"2022-03-08","cited_by_count":12,"area":null},{"id":"2106.01367","title":"On using distributed representations of source code for the detection of C security vulnerabilities","created":"2021-06-01","cited_by_count":10,"area":null},{"id":"2503.09433","title":"CASTLE: Benchmarking Dataset for Static Code Analyzers and LLMs towards CWE Detection","created":"2025-03-12","cited_by_count":10,"area":null},{"id":"2409.10756","title":"VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching","created":"2024-09-16","cited_by_count":9,"area":null},{"id":"2509.10858","title":"Large Language Models for Security Operations Centers: A Comprehensive Survey","created":"2025-09-13","cited_by_count":9,"area":null},{"id":"2308.10523","title":"When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection","created":"2023-08-21","cited_by_count":8,"area":null},{"id":"2308.14434","title":"Using ChatGPT as a Static Application Security Testing Tool","created":"2023-08-28","cited_by_count":8,"area":null},{"id":"2312.01241","title":"Just-in-Time Detection of Silent Security Patches","created":"2023-12-02","cited_by_count":7,"area":null},{"id":"2503.10784","title":"Vulnerability Detection: From Formal Verification to Large Language Models and Hybrid Approaches: A Comprehensive Overview","created":"2025-03-13","cited_by_count":7,"area":null},{"id":"2101.03118","title":"Simulating SQL Injection Vulnerability Exploitation Using Q-Learning Reinforcement Learning Agents","created":"2021-01-08","cited_by_count":6,"area":null},{"id":"2203.11790","title":"Learning Program Semantics with Code Representations: An Empirical Study","created":"2022-03-22","cited_by_count":6,"area":null},{"id":"2206.13619","title":"DeepPERF: A Deep Learning-Based Approach For Improving Software Performance","created":"2022-06-27","cited_by_count":6,"area":null},{"id":"2303.07525","title":"Automated Vulnerability Detection in Source Code Using Quantum Natural Language Processing","created":"2023-03-13","cited_by_count":6,"area":null},{"id":"2312.05275","title":"Exploring the Limits of ChatGPT in Software Security Applications","created":"2023-12-08","cited_by_count":6,"area":null},{"id":"2401.07466","title":"Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection","created":"2024-01-15","cited_by_count":6,"area":null},{"id":"2407.03093","title":"Revisiting the Performance of Deep Learning-Based Vulnerability Detection on Realistic Datasets","created":"2024-07-03","cited_by_count":6,"area":null},{"id":"2407.17053","title":"Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++","created":"2024-07-24","cited_by_count":6,"area":null},{"id":"2509.23571","title":"Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting","created":"2025-09-28","cited_by_count":6,"area":null},{"id":"2109.03341","title":"Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation","created":"2021-09-07","cited_by_count":5,"area":null},{"id":"2304.11072","title":"An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph","created":"2023-04-17","cited_by_count":5,"area":null},{"id":"2305.04940","title":"The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification","created":"2023-05-08","cited_by_count":5,"area":null},{"id":"2311.00931","title":"Learning Defect Prediction from Unrealistic Data","created":"2023-11-02","cited_by_count":5,"area":null},{"id":"2403.10646","title":"A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks","created":"2024-03-15","cited_by_count":5,"area":null},{"id":"2103.12607","title":"ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning","created":"2021-03-23","cited_by_count":4,"area":null},{"id":"2108.00669","title":"Towards Making Deep Learning-based Vulnerability Detectors Robust","created":"2021-08-02","cited_by_count":4,"area":null},{"id":"2209.07957","title":"Malicious Source Code Detection Using Transformer","created":"2022-09-16","cited_by_count":4,"area":null},{"id":"2404.04839","title":"AI for DevSecOps: A Landscape and Future Opportunities","created":"2024-04-07","cited_by_count":4,"area":null},{"id":"2411.16561","title":"EnStack: An Ensemble Stacking Framework of Large Language Models for Enhanced Vulnerability Detection in Source Code","created":"2024-11-25","cited_by_count":4,"area":null},{"id":"2412.12039","title":"Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection","created":"2024-12-16","cited_by_count":4,"area":null},{"id":"2509.19117","title":"LLM-based Vulnerability Discovery through the Lens of Code Metrics","created":"2025-09-23","cited_by_count":4,"area":null},{"id":"2212.05773","title":"A Survey on Natural Language Processing for Programming","created":"2022-12-12","cited_by_count":3,"area":null},{"id":"2306.06935","title":"LIVABLE: Exploring Long-Tailed Classification of Software Vulnerability Types","created":"2023-06-12","cited_by_count":3,"area":null},{"id":"2502.11167","title":"SURGE: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors","created":"2025-02-16","cited_by_count":3,"area":null},{"id":"2503.07770","title":"Evaluating LLaMA 3.2 for Software Vulnerability Detection","created":"2025-03-10","cited_by_count":3,"area":null},{"id":"2508.16625","title":"Data and Context Matter: Towards Generalizing AI-based Software Vulnerability Detection","created":"2025-08-14","cited_by_count":3,"area":null},{"id":"2104.11230","title":"Literature review on vulnerability detection using NLP technology","created":"2021-04-23","cited_by_count":2,"area":null},{"id":"2104.14978","title":"A comparative study of neural network techniques for automatic software vulnerability detection","created":"2021-04-29","cited_by_count":2,"area":null},{"id":"2105.03131","title":"Code2Image: Intelligent Code Analysis by Computer Vision Techniques and Application to Vulnerability Prediction","created":"2021-05-07","cited_by_count":2,"area":null},{"id":"2408.07181","title":"VulCatch: Enhancing Binary Vulnerability Detection through CodeT5 Decompilation and KAN Advanced Feature Extraction","created":"2024-08-13","cited_by_count":2,"area":null},{"id":"2509.04260","title":"An Empirical Study of Vulnerabilities in Python Packages and Their Detection","created":"2025-09-04","cited_by_count":2,"area":null},{"id":"2605.24171","title":"PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection","created":"2026-05-22","cited_by_count":2,"area":null},{"id":"2101.06319","title":"Old but Gold: Reconsidering the value of feedforward learners for software analytics","created":"2021-01-15","cited_by_count":1,"area":null},{"id":"2111.05827","title":"Data-Driven AI Model Signal-Awareness Enhancement and Introspection","created":"2021-11-10","cited_by_count":1,"area":null},{"id":"2202.02501","title":"GraphEye: A Novel Solution for Detecting Vulnerable Functions Based on Graph Attention Network","created":"2022-02-05","cited_by_count":1,"area":null},{"id":"2209.10406","title":"Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle","created":"2022-09-19","cited_by_count":1,"area":null},{"id":"2211.16235","title":"DCDetector: An IoT terminal vulnerability mining system based on distributed deep ensemble learning under source code representation","created":"2022-11-29","cited_by_count":1,"area":null},{"id":"2408.08619","title":"PatUntrack: Automated Generating Patch Examples for Issue Reports without Tracked Insecure Code","created":"2024-08-16","cited_by_count":1,"area":null},{"id":"2408.16028","title":"ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data","created":"2024-08-28","cited_by_count":1,"area":null},{"id":"2412.06166","title":"MVD: A Multi-Lingual Software Vulnerability Detection Framework","created":"2024-12-09","cited_by_count":1,"area":null},{"id":"2606.20502","title":"Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software","created":"2026-06-18","cited_by_count":1,"area":null},{"id":"2202.02679","title":"Featherweight Assisted Vulnerability Discovery","created":"2022-02-06","cited_by_count":0,"area":null},{"id":"2205.08585","title":"CV4Code: Sourcecode Understanding via Visual Code Representations","created":"2022-05-11","cited_by_count":0,"area":null},{"id":"2208.05227","title":"Multi-View Pre-Trained Model for Code Vulnerability Identification","created":"2022-08-10","cited_by_count":0,"area":null},{"id":"2209.14727","title":"FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS","created":"2022-09-29","cited_by_count":0,"area":null},{"id":"2303.06177","title":"Software Vulnerability Prediction Knowledge Transferring Between Programming Languages","created":"2023-03-10","cited_by_count":0,"area":null},{"id":"2304.03064","title":"An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset","created":"2023-04-06","cited_by_count":0,"area":null},{"id":"2305.09673","title":"Vulnerability Detection Using Two-Stage Deep Learning Models","created":"2023-05-08","cited_by_count":0,"area":null},{"id":"2306.05375","title":"Sequential Graph Neural Networks for Source Code Vulnerability Identification","created":"2023-05-23","cited_by_count":0,"area":null},{"id":"2306.06109","title":"Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2306.07981","title":"Feature Engineering-Based Detection of Buffer Overflow Vulnerability in Source Code Using Neural Networks","created":"2023-06-01","cited_by_count":0,"area":null},{"id":"2312.01588","title":"ActiveClean: Generating Line-Level Vulnerability Data via Active Learning","created":"2023-12-04","cited_by_count":0,"area":null},{"id":"2401.02686","title":"Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors","created":"2024-01-05","cited_by_count":0,"area":null},{"id":"2404.09537","title":"Machine Learning Techniques for Python Source Code Vulnerability Detection","created":"2024-04-15","cited_by_count":0,"area":null},{"id":"2406.03577","title":"Explaining the Contributing Factors for Vulnerability Detection in Machine Learning","created":"2024-06-05","cited_by_count":0,"area":null},{"id":"2407.02509","title":"Variables are a Curse in Software Vulnerability Prediction","created":"2024-06-18","cited_by_count":0,"area":null},{"id":"2409.07407","title":"CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identification","created":"2024-09-11","cited_by_count":0,"area":null},{"id":"2410.18107","title":"In-Context Code-Text Learning for Bimodal Software Engineering","created":"2024-10-08","cited_by_count":0,"area":null},{"id":"2412.11194","title":"Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points","created":"2024-12-15","cited_by_count":0,"area":null},{"id":"2505.23875","title":"A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants","created":"2025-05-29","cited_by_count":0,"area":null},{"id":"2506.10988","title":"You Only Train Once: A Flexible Training Framework for Code Vulnerability Detection Driven by Vul-Vector","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2506.11939","title":"Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches","created":"2025-06-13","cited_by_count":0,"area":null},{"id":"2507.08540","title":"White-Basilisk: A Hybrid Model for Code Vulnerability Detection","created":"2025-07-11","cited_by_count":0,"area":null},{"id":"2509.11312","title":"Weakly Supervised Vulnerability Localization via Multiple Instance Learning","created":"2025-09-14","cited_by_count":0,"area":null},{"id":"2510.04397","title":"MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2510.20739","title":"Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.js","created":"2025-10-23","cited_by_count":0,"area":null},{"id":"2512.14500","title":"C-ing Clearly: Enhanced Binary Code Explanations using C code","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2512.18542","title":"SecureCode: A Production-Grade Multi-Turn Dataset for Training Security-Aware Code Generation Models","created":"2025-12-20","cited_by_count":0,"area":null},{"id":"2601.02438","title":"Focus on What Matters: Fisher-Guided Adaptive Multimodal Fusion for Vulnerability Detection","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2604.26313","title":"VulStyle: A Multi-Modal Pre-Training for Code Stylometry-Augmented Vulnerability Detection","created":"2026-04-29","cited_by_count":0,"area":null}],"next_cursor":"100"}