{"total":47,"rows":[{"id":"2312.02120","title":"Magicoder: Empowering Code Generation with OSS-Instruct","created":"2023-12-04","cited_by_count":256,"area":null},{"id":"2405.03644","title":"When LLMs Meet Cybersecurity: A Systematic Literature Review","created":"2024-05-06","cited_by_count":71,"area":null},{"id":"2403.01632","title":"SynCode: LLM Generation with Grammar Augmentation","created":"2024-03-03","cited_by_count":69,"area":null},{"id":"2410.24198","title":"SelfCodeAlign: Self-Alignment for Code Generation","created":"2024-10-31","cited_by_count":62,"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":"2312.15698","title":"RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair","created":"2023-12-25","cited_by_count":50,"area":null},{"id":"2312.12450","title":"Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions","created":"2023-12-11","cited_by_count":48,"area":null},{"id":"2408.08313","title":"Can Large Language Models Understand Symbolic Graphics Programs?","created":"2024-08-15","cited_by_count":35,"area":null},{"id":"2504.09246","title":"Type-Constrained Code Generation with Language Models","created":"2025-04-12","cited_by_count":35,"area":null},{"id":"2410.07295","title":"IterGen: Iterative Semantic-aware Structured LLM Generation with Backtracking","created":"2024-10-09","cited_by_count":29,"area":null},{"id":"2404.18353","title":"How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models","created":"2024-04-29","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":"2405.01787","title":"Towards Neural Synthesis for SMT-Assisted Proof-Oriented Programming","created":"2024-05-03","cited_by_count":20,"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":"2503.17924","title":"WLB-LLM: Workload-Balanced 4D Parallelism for Large Language Model Training","created":"2025-03-23","cited_by_count":17,"area":null},{"id":"2310.03210","title":"Can Language Models Employ the Socratic Method? Experiments with Code Debugging","created":"2023-10-04","cited_by_count":15,"area":null},{"id":"2408.02487","title":"LiCoEval: Evaluating LLMs on License Compliance in Code Generation","created":"2024-08-05","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":"2404.08877","title":"Aligning the Objective of LLM-based Program Repair","created":"2024-04-13","cited_by_count":12,"area":null},{"id":"2402.15100","title":"Studying LLM Performance on Closed- and Open-source Data","created":"2024-02-23","cited_by_count":11,"area":null},{"id":"2407.00125","title":"A Survey on Failure Analysis and Fault Injection in AI Systems","created":"2024-06-28","cited_by_count":10,"area":null},{"id":"2408.03519","title":"RepoMasterEval: Evaluating Code Completion via Real-World Repositories","created":"2024-08-07","cited_by_count":10,"area":null},{"id":"2503.15554","title":"Rethinking the Evaluation of Secure Code Generation","created":"2025-03-18","cited_by_count":10,"area":null},{"id":"2409.00921","title":"Statically Contextualizing Large Language Models with Typed Holes","created":"2024-09-02","cited_by_count":9,"area":null},{"id":"2409.19894","title":"TransAgent: Enhancing LLM-Based Code Translation via Fine-Grained Execution Alignment","created":"2024-09-30","cited_by_count":7,"area":null},{"id":"2408.04660","title":"XMainframe: A Large Language Model for Mainframe Modernization","created":"2024-08-05","cited_by_count":6,"area":null},{"id":"2410.16655","title":"Memory-Efficient Large Language Models for Program Repair with Semantic-Guided Patch Generation","created":"2024-10-22","cited_by_count":5,"area":null},{"id":"2512.04680","title":"Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap","created":"2025-12-04","cited_by_count":5,"area":null},{"id":"2310.16253","title":"ConDefects: A New Dataset to Address the Data Leakage Concern for LLM-based Fault Localization and Program Repair","created":"2023-10-25","cited_by_count":4,"area":null},{"id":"2405.00066","title":"Research and application of artificial intelligence based webshell detection model: A literature review","created":"2024-04-28","cited_by_count":4,"area":null},{"id":"2410.22370","title":"Survey of User Interface Design and Interaction Techniques in Generative AI Applications","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2412.03905","title":"Integrating Various Software Artifacts for Better LLM-based Bug Localization and Program Repair","created":"2024-12-05","cited_by_count":4,"area":null},{"id":"2408.02402","title":"Enhancing AI-based Generation of Software Exploits with Contextual Information","created":"2024-08-05","cited_by_count":3,"area":null},{"id":"2410.07573","title":"RealVul: Can We Detect Vulnerabilities in Web Applications with LLM?","created":"2024-10-10","cited_by_count":3,"area":null},{"id":"2402.07408","title":"Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples","created":"2024-02-12","cited_by_count":2,"area":null},{"id":"2507.15157","title":"Can LLMs Generate User Stories and Assess Their Quality?","created":"2025-07-20","cited_by_count":2,"area":null},{"id":"2507.15822","title":"Do AI models help produce verified bug fixes?","created":"2025-07-21","cited_by_count":2,"area":null},{"id":"2604.17338","title":"Precise Debugging Benchmark: Is Your Model Debugging or Regenerating?","created":"2026-04-19","cited_by_count":2,"area":null},{"id":"2501.12521","title":"An Empirically-grounded tool for Automatic Prompt Linting and Repair: A Case Study on Bias, Vulnerability, and Optimization in Developer Prompts","created":"2025-01-21","cited_by_count":1,"area":null},{"id":"2504.15637","title":"DR.FIX: Automatically Fixing Data Races at Industry Scale","created":"2025-04-22","cited_by_count":1,"area":null},{"id":"2602.01187","title":"Autoregressive, Yet Revisable: In Decoding Revision for Secure Code Generation","created":"2026-02-01","cited_by_count":1,"area":null},{"id":"2606.08476","title":"FlashCP: Load-Balanced Communication-Efficient Context Parallelism for LLM Training","created":"2026-06-07","cited_by_count":1,"area":null},{"id":"2409.16416","title":"Selection of Prompt Engineering Techniques for Code Generation through Predicting Code Complexity","created":"2024-09-24","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":"2511.01196","title":"An Interdisciplinary and Cross-Task Review on Missing Data Imputation","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2601.02045","title":"The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers","created":"2026-01-05","cited_by_count":0,"area":null},{"id":"2604.01849","title":"From Guessing to Placeholding: A Cost-Theoretic Framework for Uncertainty-Aware Code Completion","created":"2026-04-02","cited_by_count":0,"area":null}],"next_cursor":null}