Cover Image

Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Fang, Liu, Yang, Shi, Lin, Yang, Zhen, Zhang, Li, Lian, Xiaoli, Li, Zhongqi, Ma, Yuchi
Format:	Preprint
Published:	2024
Subjects:	Software Engineering Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
by: Wang, Yanlin, et al.
Published: (2024)

FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification
by: Zhao, Qianhui, et al.
Published: (2025)

Enhancing Automated Program Repair with Solution Design
by: Zhao, Jiuang, et al.
Published: (2024)

Detecting and Correcting Hallucinations in LLM-Generated Code via Deterministic AST Analysis
by: Khati, Dipin, et al.
Published: (2026)

Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
by: Yang, Zhen, et al.
Published: (2024)

SecureReviewer: Enhancing Large Language Models for Secure Code Review through Secure-aware Fine-tuning
by: Liu, Fang, et al.
Published: (2025)

Eliminating Hallucination-Induced Errors in LLM Code Generation with Functional Clustering
by: Ravuri, Chaitanya, et al.
Published: (2025)

AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers
by: Yang, Donghao, et al.
Published: (2024)

Towards Mitigating API Hallucination in Code Generated by LLMs with Hierarchical Dependency Aware
by: Chen, Yujia, et al.
Published: (2025)

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair
by: Liu, Simiao, et al.
Published: (2026)

Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification
by: Liu, Aofan, et al.
Published: (2025)

Hallucinations in Code Change to Natural Language Generation: Prevalence and Evaluation of Detection Metrics
by: Liu, Chunhua, et al.
Published: (2025)

Top General Performance = Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark
by: Zheng, Dewu, et al.
Published: (2024)

Unseen Horizons: Unveiling the Real Capability of LLM Code Generation Beyond the Familiar
by: Zhang, Yuanliang, et al.
Published: (2024)

From Context to Intent: Reasoning-Guided Function-Level Code Completion
by: Li, Yanzhou, et al.
Published: (2025)

Hallucination in LLM-Based Code Generation: An Automotive Case Study
by: Pavel, Marc, et al.
Published: (2025)

CodeCircuit: Toward Inferring LLM-Generated Code Correctness via Attribution Graphs
by: He, Yicheng, et al.
Published: (2026)

Towards Better Correctness and Efficiency in Code Generation
by: Feng, Yunlong, et al.
Published: (2025)

Schedule-and-Calibrate: Utility-Guided Multi-Task Reinforcement Learning for Code LLMs
by: Chen, Yujia, et al.
Published: (2026)

CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
by: Wang, Peiding, et al.
Published: (2025)

ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation
by: Liu, Sicong, et al.
Published: (2026)

LLM4EFFI: Leveraging Large Language Models to Enhance Code Efficiency and Correctness
by: Ye, Tong, et al.
Published: (2025)

Beyond Functional Correctness: Design Issues in AI IDE-Generated Large-Scale Projects
by: Kashif, Syed Mohammad, et al.
Published: (2026)

NARRepair: Non-Autoregressive Code Generation Model for Automatic Program Repair
by: Yang, Zhenyu, et al.
Published: (2024)

VerilogReader: LLM-Aided Hardware Test Generation
by: Ma, Ruiyang, et al.
Published: (2024)

LLM Hallucinations in Practical Code Generation: Phenomena, Mechanism, and Mitigation
by: Zhang, Ziyao, et al.
Published: (2024)

Beyond Autoregression: An Empirical Study of Diffusion Large Language Models for Code Generation
by: Li, Chengze, et al.
Published: (2025)

Code Hallucination
by: Rahman, Mirza Masfiqur, et al.
Published: (2024)

Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes
by: Chon, Heejae, et al.
Published: (2024)

Personality-Guided Code Generation Using Large Language Models
by: Guo, Yaoqi, et al.
Published: (2024)

HLSDebugger: Identification and Correction of Logic Bugs in HLS Code with LLM Solutions
by: Wang, Jing, et al.
Published: (2025)

RobuNFR: Evaluating the Robustness of Large Language Models on Non-Functional Requirements Aware Code Generation
by: Lin, Feng, et al.
Published: (2025)

DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs
by: Liu, Zhihan, et al.
Published: (2024)

CodeMirage: Hallucinations in Code Generated by Large Language Models
by: Agarwal, Vibhor, et al.
Published: (2024)

Beyond Correctness: Benchmarking Multi-dimensional Code Generation for Large Language Models
by: Zheng, Jiasheng, et al.
Published: (2024)

Faver: Boosting LLM-based RTL Generation with Function Abstracted Verifiable Middleware
by: Mu, Jianan, et al.
Published: (2025)

An Empirical Study on Failures in Automated Issue Solving
by: Liu, Simiao, et al.
Published: (2025)

Uncertainty Quantification for LLM-based Code Generation
by: Xu, Senrong, et al.
Published: (2026)

Tree-of-Code: A Tree-Structured Exploring Framework for End-to-End Code Generation and Execution in Complex Task Handling
by: Ni, Ziyi, et al.
Published: (2024)

Correctness isnt Efficiency: Runtime Memory Divergence in LLM-Generated Code
by: Rajput, Prateek, et al.
Published: (2026)