{"total":103,"rows":[{"id":"2305.01210","title":"Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation","created":"2023-05-02","cited_by_count":1391,"area":null},{"id":"2407.01489","title":"Agentless: Demystifying LLM-based Software Engineering Agents","created":"2024-07-01","cited_by_count":421,"area":"agents"},{"id":"2308.10620","title":"Large Language Models for Software Engineering: A Systematic Literature Review","created":"2023-08-21","cited_by_count":399,"area":null},{"id":"2409.02977","title":"Large Language Model-Based Agents for Software Engineering: A Survey","created":"2024-09-04","cited_by_count":178,"area":"agents"},{"id":"2511.13646","title":"Live-SWE-agent: Can Software Engineering Agents Self-Evolve on the Fly?","created":"2025-11-17","cited_by_count":95,"area":"agents"},{"id":"2401.10034","title":"Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap","created":"2024-01-18","cited_by_count":90,"area":null},{"id":"2308.04748","title":"Fuzz4All: Universal Fuzzing with Large Language Models","created":"2023-08-09","cited_by_count":88,"area":null},{"id":"2506.11791","title":"SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks","created":"2025-06-13","cited_by_count":75,"area":"agents"},{"id":"2405.03644","title":"When LLMs Meet Cybersecurity: A Systematic Literature Review","created":"2024-05-06","cited_by_count":71,"area":null},{"id":"2308.03314","title":"GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis","created":"2023-08-07","cited_by_count":65,"area":null},{"id":"2504.04030","title":"OpenCodeInstruct: A Large-scale Instruction Tuning Dataset for Code LLMs","created":"2025-04-05","cited_by_count":60,"area":null},{"id":"2401.16185","title":"LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning","created":"2024-01-29","cited_by_count":59,"area":"lr"},{"id":"2309.00608","title":"Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair","created":"2023-09-01","cited_by_count":55,"area":null},{"id":"2308.03656","title":"Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench","created":"2023-08-07","cited_by_count":54,"area":null},{"id":"2309.07062","title":"Large Language Models for Compiler Optimization","created":"2023-09-11","cited_by_count":52,"area":null},{"id":"2409.13082","title":"AutoVerus: Automated Proof Generation for Rust Code","created":"2024-09-19","cited_by_count":49,"area":null},{"id":"2305.14752","title":"A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification","created":"2023-05-24","cited_by_count":42,"area":null},{"id":"2408.05534","title":"Can LLMs Replace Manual Annotation of Software Engineering Artifacts?","created":"2024-08-10","cited_by_count":42,"area":null},{"id":"2403.19114","title":"Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM","created":"2024-03-28","cited_by_count":40,"area":null},{"id":"2405.18649","title":"LeDex: Training LLMs to Better Self-Debug and Explain Code","created":"2024-05-28","cited_by_count":40,"area":null},{"id":"2407.02524","title":"Meta Large Language Model Compiler: Foundation Models of Compiler Optimization","created":"2024-06-27","cited_by_count":39,"area":null},{"id":"2310.15991","title":"WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models","created":"2023-10-24","cited_by_count":37,"area":null},{"id":"2405.02580","title":"PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation","created":"2024-05-04","cited_by_count":37,"area":"rag"},{"id":"2503.09002","title":"KNighter: Transforming Static Analysis with LLM-Synthesized Checkers","created":"2025-03-12","cited_by_count":24,"area":null},{"id":"2402.10754","title":"LLMDFA: Analyzing Dataflow in Code with Large Language Models","created":"2024-02-16","cited_by_count":22,"area":null},{"id":"2504.05408","title":"Frontier AI's Impact on the Cybersecurity Landscape","created":"2025-04-07","cited_by_count":21,"area":null},{"id":"2405.04520","title":"NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Prompts","created":"2024-05-07","cited_by_count":19,"area":null},{"id":"2503.04479","title":"ToolFuzz -- Automated Agent Tool Testing","created":"2025-03-06","cited_by_count":18,"area":"lr"},{"id":"2509.01835","title":"From CVE Entries to Verifiable Exploits: An Automated Multi-Agent Framework for Reproducing CVEs","created":"2025-09-01","cited_by_count":18,"area":"agents"},{"id":"2401.00563","title":"KernelGPT: Enhanced Kernel Fuzzing via Large Language Models","created":"2023-12-31","cited_by_count":17,"area":null},{"id":"2305.19926","title":"Revisiting the Reliability of Psychological Scales on Large Language Models","created":"2023-05-31","cited_by_count":16,"area":null},{"id":"2402.00093","title":"ChIRAAG: ChatGPT Informed Rapid and Automated Assertion Generation","created":"2024-01-31","cited_by_count":16,"area":null},{"id":"2506.17644","title":"Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges","created":"2025-06-21","cited_by_count":15,"area":null},{"id":"2406.07714","title":"LLAMAFUZZ: Large Language Model Enhanced Greybox Fuzzing","created":"2024-06-11","cited_by_count":14,"area":null},{"id":"2410.21060","title":"CTINexus: Automatic Cyber Threat Intelligence Knowledge Graph Construction Using Large Language Models","created":"2024-10-28","cited_by_count":14,"area":null},{"id":"2409.09464","title":"Measuring the Influence of Incorrect Code on Test Generation","created":"2024-09-14","cited_by_count":11,"area":null},{"id":"2512.07533","title":"VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection","created":"2025-12-08","cited_by_count":11,"area":"lr"},{"id":"2408.03519","title":"RepoMasterEval: Evaluating Code Completion via Real-World Repositories","created":"2024-08-07","cited_by_count":10,"area":null},{"id":"2403.14714","title":"Compiler generated feedback for Large Language Models","created":"2024-03-18","cited_by_count":9,"area":null},{"id":"2408.05715","title":"Top Pass: Improve Code Generation by Pass@k-Maximized Code Ranking","created":"2024-08-11","cited_by_count":9,"area":null},{"id":"2508.21302","title":"Locus: Agentic Predicate Synthesis for Directed Fuzzing","created":"2025-08-29","cited_by_count":9,"area":"agents"},{"id":"2309.09980","title":"Code Representation Pre-training with Complements from Program Executions","created":"2023-09-04","cited_by_count":8,"area":null},{"id":"2402.18734","title":"Priority Sampling of Large Language Models for Compilers","created":"2024-02-28","cited_by_count":8,"area":null},{"id":"2404.16297","title":"When Fuzzing Meets LLMs: Challenges and Opportunities","created":"2024-04-25","cited_by_count":8,"area":null},{"id":"2408.07846","title":"A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites","created":"2024-08-14","cited_by_count":8,"area":null},{"id":"2508.00408","title":"Benchmarking LLMs for Unit Test Generation from Real-World Functions","created":"2025-08-01","cited_by_count":8,"area":null},{"id":"2302.02261","title":"NeuRI: Diversifying DNN Generation via Inductive Rule Inference","created":"2023-02-04","cited_by_count":7,"area":null},{"id":"2502.00735","title":"`From Prompt to Perturbation': An Adaptive Framework for Voice-Based Jailbreaks on Audio LLMs","created":"2025-02-02","cited_by_count":7,"area":"safety"},{"id":"2504.18050","title":"Validating Network Protocol Parsers with Traceable RFC Document Interpretation","created":"2025-04-25","cited_by_count":7,"area":null},{"id":"2311.07377","title":"Testing learning-enabled cyber-physical systems with Large-Language Models: A Formal Approach","created":"2023-11-13","cited_by_count":5,"area":null},{"id":"2404.17871","title":"Deep Learning Library Testing: Definition, Methods and Challenges","created":"2024-04-27","cited_by_count":5,"area":null},{"id":"2406.07944","title":"Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries","created":"2024-06-12","cited_by_count":5,"area":null},{"id":"2504.13515","title":"Large Language Models for Validating Network Protocol Parsers","created":"2025-04-18","cited_by_count":5,"area":null},{"id":"2507.22462","title":"IFEvalCode: Controlled Code Generation","created":"2025-07-30","cited_by_count":5,"area":null},{"id":"2608.23564","title":"SWE Refactor Bench: Can Coding Agents Complete a Long-Horizon, Whole-Repository Stack Migration?","created":"2026-08-24","cited_by_count":5,"area":"agents"},{"id":"2402.00350","title":"On the Challenges of Fuzzing Techniques via Large Language Models","created":"2024-02-01","cited_by_count":4,"area":null},{"id":"2512.03420","title":"HarnessAgent: Scaling Automatic Fuzzing Harness Construction with Tool-Augmented LLM Pipelines","created":"2025-12-03","cited_by_count":4,"area":"agents"},{"id":"2604.11950","title":"AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection","created":"2026-04-13","cited_by_count":4,"area":"agents"},{"id":"2605.16299","title":"ACE: Self-Evolving LLM Coding Framework via Adversarial Unit Test Generation and Preference Optimization","created":"2026-04-17","cited_by_count":4,"area":"alignment"},{"id":"2402.12222","title":"CovRL: Fuzzing JavaScript Engines with Coverage-Guided Reinforcement Learning for LLM-based Mutation","created":"2024-02-19","cited_by_count":3,"area":null},{"id":"2605.15301","title":"Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution","created":"2026-05-14","cited_by_count":3,"area":"agents"},{"id":"2310.06433","title":"Retromorphic Testing: A New Approach to the Test Oracle Problem","created":"2023-10-10","cited_by_count":2,"area":null},{"id":"2404.03664","title":"LLMs in the Heart of Differential Testing: A Case Study on a Medical Rule Engine","created":"2024-02-16","cited_by_count":2,"area":null},{"id":"2404.09151","title":"Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging","created":"2024-04-14","cited_by_count":2,"area":null},{"id":"2404.09384","title":"Generative transformations and patterns in LLM-native approaches for software verification and falsification","created":"2024-04-14","cited_by_count":2,"area":null},{"id":"2411.10842","title":"CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit","created":"2024-11-16","cited_by_count":2,"area":null},{"id":"2412.06684","title":"Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach","created":"2024-12-09","cited_by_count":2,"area":null},{"id":"2501.12339","title":"Treefix: Enabling Execution with a Tree of Prefixes","created":"2025-01-21","cited_by_count":2,"area":null},{"id":"2507.22065","title":"Fuzzing: Randomness? Reasoning! Efficient Directed Fuzzing via Large Language Models","created":"2025-06-30","cited_by_count":2,"area":"lr"},{"id":"2607.02345","title":"SkillFuzz: Fuzzing Skill Composition for Implicit Intents Discovery in Open Skill Marketplaces","created":"2026-07-02","cited_by_count":2,"area":null},{"id":"2607.06195","title":"LogicHunter: Testing LLM Agent Frameworks with an Agentic Oracle","created":"2026-07-07","cited_by_count":2,"area":"agents"},{"id":"2412.12163","title":"Towards LLM-based optimization compilers. Can LLMs learn how to apply a single peephole optimization? Reasoning is all LLMs need!","created":"2024-12-11","cited_by_count":1,"area":"lr"},{"id":"2412.19088","title":"Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain Security","created":"2024-12-26","cited_by_count":1,"area":null},{"id":"2506.15648","title":"deepSURF: Detecting Memory Safety Vulnerabilities in Rust Through Fuzzing LLM-Augmented Harnesses","created":"2025-06-18","cited_by_count":1,"area":null},{"id":"2506.17369","title":"Re-Evaluating Code LLM Benchmarks Under Semantic Mutation","created":"2025-06-20","cited_by_count":1,"area":null},{"id":"2510.02694","title":"MALF: A Multi-Agent LLM Framework for Intelligent Fuzzing of Industrial Control Protocols","created":"2025-10-03","cited_by_count":1,"area":"agents"},{"id":"2511.20403","title":"LLMs for Automated Unit Test Generation and Assessment in Java: The AgoneTest Framework","created":"2025-11-25","cited_by_count":1,"area":null},{"id":"2604.24212","title":"Empowering Autonomous Debugging Agents with Efficient Dynamic Analysis","created":"2026-04-27","cited_by_count":1,"area":"agents"},{"id":"2605.11202","title":"Continuous Discovery of Vulnerabilities in LLM Serving Systems with Fuzzing","created":"2026-05-11","cited_by_count":1,"area":"inference"},{"id":"2605.15229","title":"PBT-Bench: Benchmarking AI Agents on Property-Based Testing","created":"2026-05-13","cited_by_count":1,"area":"agents"},{"id":"2606.22263","title":"Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases","created":"2026-06-20","cited_by_count":1,"area":"agents"},{"id":"2407.02791","title":"Model-Enhanced LLM-Driven VUI Testing of VPA Apps","created":"2024-07-03","cited_by_count":0,"area":null},{"id":"2503.22575","title":"On the Mistaken Assumption of Interchangeable Deep Reinforcement Learning Implementations","created":"2025-03-28","cited_by_count":0,"area":"deeprl"},{"id":"2507.04055","title":"Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAG","created":"2025-07-05","cited_by_count":0,"area":null},{"id":"2507.13629","title":"Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2508.14300","title":"MultiFuzz: A Dense Retrieval-based Multi-Agent System for Network Protocol Fuzzing","created":"2025-08-19","cited_by_count":0,"area":"agents"},{"id":"2509.19136","title":"On the Soundness and Consistency of LLM Agents for Executing Test Cases Written in Natural Language","created":"2025-09-23","cited_by_count":0,"area":"agents"},{"id":"2510.10179","title":"LLMs are All You Need? Improving Fuzz Testing for MOJO with Large Language Models","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2510.10407","title":"PrediQL: Automated Testing of GraphQL APIs with LLMs","created":"2025-10-12","cited_by_count":0,"area":"rag"},{"id":"2511.01183","title":"QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code","created":"2025-11-03","cited_by_count":0,"area":"synth"},{"id":"2512.18102","title":"From Coverage to Causes: Data-Centric Fuzzing for JavaScript Engines","created":"2025-12-19","cited_by_count":0,"area":null},{"id":"2605.05000","title":"Agentic Vulnerability Reasoning on COTS Binaries","created":"2026-05-06","cited_by_count":0,"area":"agents"},{"id":"2605.14431","title":"FuzzAgent: Multi-Agent System for Evolutionary Library Fuzzing","created":"2026-05-14","cited_by_count":0,"area":"agents"},{"id":"2606.26490","title":"An Empirical Study of LLM-Generated Specifications for VeriFast","created":"2026-06-25","cited_by_count":0,"area":null},{"id":"2606.30689","title":"Citation Discipline in Spec-Driven Development: A Cross-Model Empirical Study of Output Determinism and Automated Hallucination Detection in LLM-Generated Code","created":"2026-06-28","cited_by_count":0,"area":null},{"id":"2608.03743","title":"Can LLMs Test Terminal User Interfaces?","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2608.07947","title":"Directed Neuro-Symbolic Stochastic Execution for Verification of Distributed Parallel AI Programs","created":"2026-08-08","cited_by_count":0,"area":null},{"id":"2608.13450","title":"LLM-Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.07434","title":"CIT-CAD: Constraint Intent Tree-based CAD Code Generation and Verification","created":"2026-09-07","cited_by_count":0,"area":null},{"id":"2609.22991","title":"Silent Failures Beyond the 32-Bit Index Range: A Differential Characterization of Large-Tensor Matrix Multiplication in PyTorch's MPS Backend","created":"2026-09-19","cited_by_count":0,"area":null}],"next_cursor":"100"}