5 citations · 25 across the 19 of their papers we have counts for
19 papers
Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation
Yiheng Shen, Wei Zheng, Xiao Wei +3
Large Language Models (LLMs) have shown remarkable potential in Verilog code generation, yet existing datasets contain con siderable noise and redundancy. Prior data selection meth…
Unsaid, Unsafe? Implicit Security Obligations in LLM-Based RTL Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Large Language Models (LLMs) generate register-transfer-level (RTL) code with rapidly improving functional correctness. Security of LLM-generated code, however, has been studied ma…
Execution-Anchored Hallucination Calibration Reranking for Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +2
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, yet their performance degrades significantly on low-resource Hardware Description Languag…
Semantic Consensus Decoding: Backdoor Defense for Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Large language models (LLMs) for Verilog code generation are increasingly adopted in hardware design, yet remain vulnerable to backdoor attacks where adversaries inject malicious t…
Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead
Guang Yang, Wei Zheng, Xiang Chen +14
Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI), attracting significant attention from bot…
The Cream Rises to the Top: Efficient Reranking Method for Verilog Code Generation
Guang Yang, Wei Zheng, Xiang Chen +3
LLMs face significant challenges in Verilog generation due to limited domain-specific knowledge. While sampling techniques improve pass@k metrics, hardware engineers need one trust…
CODE-DITING: A Reasoning-Based Metric for Functional Alignment in Code Evaluation
Guang Yang, Yu Zhou, Xiang Chen +5
Trustworthy evaluation methods for code snippets play a crucial role in neural code generation. Traditional methods, which either rely on reference solutions or require executable…
Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss
Guang Yang, Yu Zhou, Xiang Chen +4
Code Language Models (CLMs), particularly those leveraging deep learning, have achieved significant success in code intelligence domain. However, the issue of security, particularl…
CodeScore-R: An Automated Robustness Metric for Assessing the FunctionalCorrectness of Code Synthesis
Guang Yang, Yu Zhou, Xiang Chen +1
Evaluation metrics are crucial in the field of code synthesis. Commonly used code evaluation metrics canbe classified into three types: match-based, semantic-based, and execution-b…
Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models
Guang Yang, Yu Zhou, Xiang Chen +3
Large Language Models (LLMs) have demonstrated remarkable potential in code generation. The integration of Chain of Thought (CoT) reasoning can further boost their performance. How…
Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation
Guang Yang, Yu Zhou, Xiangyu Zhang +3
Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particula…
A Syntax-Guided Multi-Task Learning Approach for Turducken-Style Code Generation
Guang Yang, Yu Zhou, Xiang Chen +4
Due to the development of pre-trained language models, automated code generation techniques have shown great promise in recent years. However, the generated code is difficult to me…
How Important are Good Method Names in Neural Code Generation? A Model Robustness Perspective
Guang Yang, Yu Zhou, Wenhua Yang +3
Pre-trained code generation models (PCGMs) have been widely applied in neural code generation which can generate executable code from functional descriptions in natural languages,…
BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT
Chi Yu, Guang Yang, Xiang Chen +2
Developers use shell commands for many tasks, such as file system management, network control, and process management. Bash is one of the most commonly used shells and plays an imp…
SOTitle: A Transformer-based Post Title Generation Approach for Stack Overflow
Ke Liu, Guang Yang, Xiang Chen +1
On Stack Overflow, developers can not only browse question posts to solve their programming problems but also gain expertise from the question posts to help improve their programmi…
DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning
Guang Yang, Xiang Chen, Yanlin Zhou +1
A shellcode is a small piece of code and it is executed to exploit a software vulnerability, which allows the target computer to execute arbitrary commands from the attacker throug…
DeepSCC: Source Code Classification Based on Fine-Tuned RoBERTa
Guang Yang, Yanlin Zhou, Chi Yu +1
In software engineering-related tasks (such as programming language tag prediction based on code snippets from Stack Overflow), the programming language classification for code sni…
ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation
Guang Yang, Xiang Chen, Jinxin Cao +4
Developers often write low-quality code comments due to the lack of programming experience, which can reduce the efficiency of developers program comprehension. Therefore, develope…
Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer
Guang Yang, Yanlin Zhou, Xiang Chen +1
Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the…