22 citations · 29 across the 5 of their papers we have counts for
5 papers
AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation
Dong Huang, Jie M. Zhang, Michael Luck +3
The advancement of natural language processing (NLP) has been significantly boosted by the development of transformer-based large language models (LLMs). These models have revoluti…
CodeCoT: Tackling Code Syntax Errors in CoT Reasoning for Code Generation
Dong Huang, Qingwen Bu, Yuhao Qing +1
Chain-of-thought (CoT) has emerged as a groundbreaking tool in NLP, notably for its efficacy in complex reasoning tasks, such as mathematical proofs. However, its application in co…
Feature Map Testing for Deep Neural Networks
Dong Huang, Qingwen Bu, Yahao Qing +2
Due to the widespread application of deep neural networks~(DNNs) in safety-critical tasks, deep learning testing has drawn increasing attention. During the testing process, test ca…
Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing
Dong Huang, Qingwen Bu, Yichao Fu +3
Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect…
Two Heads are Better than One: Robust Learning Meets Multi-branch Models
Zongyuan Zhang, Qingwen Bu, Tianyang Duan +5
Deep neural networks (DNNs) are vulnerable to adversarial examples, in which DNNs are misled to false outputs due to inputs containing imperceptible perturbations. Adversarial trai…