Cover Image

CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Qiu, Ruidi, Zhang, Grace Li, Drechsler, Rolf, Schlichtmann, Ulf, Li, Bing
Format:	Preprint
Published:	2024
Subjects:	Software Engineering
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

AutoBench: Automatic Testbench Generation and Evaluation Using LLMs for HDL Design
by: Qiu, Ruidi, et al.
Published: (2024)

CorrectHDL: Agentic HDL Design with LLMs Leveraging High-Level Synthesis as Reference
by: Xu, Kangwei, et al.
Published: (2025)

HLSTester: Efficient Testing of Behavioral Discrepancies with LLMs for High-Level Synthesis
by: Xu, Kangwei, et al.
Published: (2025)

VFocus: Better Verilog Generation from Large Language Model via Focused Reasoning
by: Zhao, Zhuorui, et al.
Published: (2025)

Log Parsing using LLMs with Self-Generated In-Context Learning and Self-Correction
by: Wu, Yifan, et al.
Published: (2024)

LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations
by: He, Ruidi, et al.
Published: (2025)

Classification-Based Automatic HDL Code Generation Using LLMs
by: Sun, Wenhao, et al.
Published: (2024)

Paradigm-Based Automatic HDL Code Generation Using LLMs
by: Sun, Wenhao, et al.
Published: (2025)

DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models
by: Chen, Yunnong, et al.
Published: (2025)

FrontendBench: A Benchmark for Evaluating LLMs on Front-End Development via Automatic Evaluation
by: Zhu, Hongda, et al.
Published: (2025)

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

TOGLL: Correct and Strong Test Oracle Generation with LLMs
by: Hossain, Soneya Binta, et al.
Published: (2024)

Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
by: Liu, Fang, et al.
Published: (2024)

Statistical Confidence in Functional Correctness: An Approach for AI Product Functional Correctness Evaluation
by: Albertini, Wallace, et al.
Published: (2026)

When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents?
by: Peng, Yibo, et al.
Published: (2025)

PatchZero: Zero-Shot Automatic Patch Correctness Assessment
by: Zhou, Xin, et al.
Published: (2023)

EffiBench: Benchmarking the Efficiency of Automatically Generated Code
by: Huang, Dong, et al.
Published: (2024)

Lightweight Model Editing for LLMs to Correct Deprecated API Recommendations
by: Lin, Guancheng, et al.
Published: (2025)

AL-Bench: A Benchmark for Automatic Logging
by: Tan, Boyin, et al.
Published: (2025)

FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation
by: Zhou, Zhiping, et al.
Published: (2025)

AdverMCTS: Combating Pseudo-Correctness in Code Generation via Adversarial Monte Carlo Tree Search
by: Li, Qingyao, et al.
Published: (2026)

Evaluating LLMs Effectiveness in Detecting and Correcting Test Smells: An Empirical Study
by: Santana Jr, E. G., et al.
Published: (2025)

Are LLMs Correctly Integrated into Software Systems?
by: Shao, Yuchen, et al.
Published: (2024)

LLM-based Behaviour Driven Development for Hardware Design
by: Drechsler, Rolf, et al.
Published: (2025)

Correctness Witnesses with Function Contracts
by: Heizmann, Matthias, et al.
Published: (2025)

Ensuring Functional Correctness of Large Code Models with Selective Generation
by: Jeong, Jaewoo, et al.
Published: (2025)

SemGuard: Real-Time Semantic Evaluator for Correcting LLM-Generated Code
by: Wang, Qinglin, et al.
Published: (2025)

CodeScore-R: An Automated Robustness Metric for Assessing the FunctionalCorrectness of Code Synthesis
by: Yang, Guang, et al.
Published: (2024)

A Novel HDL Code Generator for Effectively Testing FPGA Logic Synthesis Compilers
by: Xu, Zhihao, et al.
Published: (2024)

On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation
by: Naik, Atharva
Published: (2024)

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code
by: Das, Susmita, et al.
Published: (2025)

Ensemble-Based Uncertainty Estimation for Code Correctness Estimation
by: Wei, Yunxiang, et al.
Published: (2026)

CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing
by: He, Xinyi, et al.
Published: (2024)

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
by: Chou, Jason, et al.
Published: (2025)

OSS-Bench: Benchmark Generator for Coding LLMs
by: Jiang, Yuancheng, et al.
Published: (2025)

FunPRM: Function-as-Step Process Reward Model with Meta Reward Correction for Code Generation
by: Zhang, Ruiyi, et al.
Published: (2026)

Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
by: Allamanis, Miltiadis, et al.
Published: (2024)

MRG-Bench: Evaluating and Exploring the Requirements of Context for Repository-Level Code Generation
by: Li, Haiyang
Published: (2025)

JunoBench: A Benchmark Dataset of Crashes in Python Machine Learning Jupyter Notebooks
by: Wang, Yiran, et al.
Published: (2025)

Automatic Generation of Formal Specification and Verification Annotations Using LLMs and Test Oracles
by: Faria, João Pascoal, et al.
Published: (2026)