{"total":35,"rows":[{"id":"2404.00971","title":"Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code","created":"2024-04-01","cited_by_count":107,"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":"2311.07989","title":"Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code","created":"2023-11-14","cited_by_count":62,"area":null},{"id":"2406.11927","title":"On the Impacts of Contexts on Repository-Level Code Generation","created":"2024-06-17","cited_by_count":22,"area":null},{"id":"2501.12934","title":"Correctness Assessment of Code Generated by Large Language Models Using Internal Representations","created":"2025-01-22","cited_by_count":17,"area":null},{"id":"2403.15600","title":"Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers","created":"2024-03-22","cited_by_count":13,"area":null},{"id":"2411.00932","title":"LLMs: A Game-Changer for Software Engineers?","created":"2024-11-01","cited_by_count":11,"area":null},{"id":"2412.15004","title":"From Vulnerabilities to Remediation: A Systematic Literature Review of LLMs in Code Security","created":"2024-12-19","cited_by_count":10,"area":null},{"id":"2504.00018","title":"SandboxEval: Towards Securing Test Environment for Untrusted Code","created":"2025-03-27","cited_by_count":10,"area":null},{"id":"2502.08108","title":"Generative AI and Empirical Software Engineering: A Paradigm Shift","created":"2025-02-12","cited_by_count":9,"area":null},{"id":"2504.14640","title":"Risk Assessment Framework for Code LLMs via Leveraging Internal States","created":"2025-04-20","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":"2503.10452","title":"DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation","created":"2025-03-13","cited_by_count":7,"area":null},{"id":"2505.10494","title":"Can You Really Trust Code Copilots? Evaluating Large Language Models from a Code Security Perspective","created":"2025-05-15","cited_by_count":6,"area":null},{"id":"2308.13507","title":"Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code","created":"2023-08-25","cited_by_count":5,"area":null},{"id":"2505.23932","title":"SwingArena: Competitive Programming Arena for Long-context GitHub Issue Solving","created":"2025-05-29","cited_by_count":5,"area":null},{"id":"2312.04687","title":"LLM4TDD: Best Practices for Test Driven Development Using Large Language Models","created":"2023-12-07","cited_by_count":4,"area":null},{"id":"2407.12220","title":"Questionable practices in machine learning","created":"2024-07-17","cited_by_count":4,"area":null},{"id":"2502.02368","title":"Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects","created":"2025-02-04","cited_by_count":4,"area":null},{"id":"2410.09268","title":"One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks","created":"2024-10-11","cited_by_count":3,"area":null},{"id":"2408.00435","title":"A Qualitative Study on Using ChatGPT for Software Security: Perception vs. Practicality","created":"2024-08-01","cited_by_count":2,"area":null},{"id":"2504.16485","title":"On Developers' Self-Declaration of AI-Generated Code: An Analysis of Practices","created":"2025-04-23","cited_by_count":1,"area":null},{"id":"2508.19472","title":"SIExVulTS: Sensitive Information Exposure Vulnerability Detection System using Transformer Models and Static Analysis","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2509.20277","title":"Investigating Security Implications of Automatically Generated Code on the Software Supply Chain","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2605.05267","title":"Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code","created":"2026-05-06","cited_by_count":1,"area":null},{"id":"2606.09800","title":"FASE: Fast Adaptive Semantic Entropy for Code Quality","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2607.03174","title":"Effectiveness of LLM-based Software Diversity for Reliability Improvement -- an Empirical Study","created":"2026-07-03","cited_by_count":1,"area":null},{"id":"2410.02156","title":"The why, what, and how of AI-based coding in scientific research","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2511.03549","title":"Uncovering Code Insights: Leveraging GitHub Artifacts for Deeper Code Understanding","created":"2025-11-05","cited_by_count":0,"area":null},{"id":"2603.11212","title":"Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2606.21171","title":"An Exploratory Case Study of LLM-Assisted Refactoring and Gameplay Feature Generation in an Endless Runner Game","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.11348","title":"Understanding the Impact of AI Code Assistants on Security API Usage: An Empirical Study","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2607.12273","title":"Code-MUE: Measuring Code LLMs' Uncertainty through Execution-based Semantic Interaction Graphs","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2609.01601","title":"Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2610.15482","title":"Security in LLM-Assisted Coding: A Systematic Literature Review","created":"2026-10-11","cited_by_count":0,"area":null}],"next_cursor":null}