5 citations · 26 across the 15 of their papers we have counts for
15 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…
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…
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…
Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimization
Chaoyang Gao, Xiang Chen, Jiyu Wang +2
The increasing complexity of software systems has led to a surge in cybersecurity vulnerabilities, necessitating efficient and scalable solutions for vulnerability assessment. Howe…
Automatic Bi-modal Question Title Generation for Stack Overflow with Prompt Learning
Shaoyu Yang, Xiang Chen, Ke Liu +2
When drafting question posts for Stack Overflow, developers may not accurately summarize the core problems in the question titles, which can cause these questions to not get timely…
Automatic Smart Contract Comment Generation via Large Language Models and In-Context Learning
Junjie Zhao, Xiang Chen, Guang Yang +1
The previous smart contract code comment (SCC) generation approaches can be divided into two categories: fine-tuning paradigm-based approaches and information retrieval-based appro…
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…