most citedAssessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation

1 citations · 1 across the 5 of their papers we have counts for

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cs.SE2024

SimADFuzz: Simulation-Feedback Fuzz Testing for Autonomous Driving Systems

Huiwen Yang, Yu Zhou, Taolue Chen

Autonomous driving systems (ADS) have achieved remarkable progress in recent years. However, ensuring their safety and reliability remains a critical challenge due to the complexit…

cs.SE2023

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…

cs.SE2023★ 1 cited

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…

cs.SE2023

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…

cs.SE2023

Test Reuse Based on Adaptive Semantic Matching across Android Mobile Applications

Shuqi Liu, Yu Zhou, Tingting Han +1

Automatic test generation can help verify and develop the behavior of mobile applications. Test reuse based on semantic similarities between applications of the same category has b…