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20082024
most citedAdversarial Robustness of Deep Code Comment Generation

49 citations · 223 across the 35 of their papers we have counts for

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9 papers · 1 filter

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…

cs.SE2022★ 1 cited

DRIVE: Dockerfile Rule Mining and Violation Detection

Yu Zhou, Weilin Zhan, Zi Li +3

A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable…

cs.SE2022★ 5 cited

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,…

cs.SE2021★ 49 cited

Adversarial Robustness of Deep Code Comment Generation

Yu Zhou, Xiaoqing Zhang, Juanjuan Shen +3

Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they als…

cs.SE2020★ 44 cited

Boosting API Recommendation with Implicit Feedback

Yu Zhou, Xinying Yang, Taolue Chen +3

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the…

cs.SE2019

Model-based Automated Testing of JavaScript Web Applications via Longer Test Sequences

Pengfei Gao, Fu Song, Taolue Chen +2

JavaScript has become one of the most widely used languages for Web development. However, it is challenging to ensure the correctness and reliability of Web applications written in…

cs.SE2019★ 42 cited

User Review-Based Change File Localization for Mobile Applications

Yu Zhou, Yanqi Su, Taolue Chen +3

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespre…