18 citations · 48 across the 8 of their papers we have counts for
9 papers
CKGFuzzer: LLM-Based Fuzz Driver Generation Enhanced By Code Knowledge Graph
Hanxiang Xu, Wei Ma, Ting Zhou +5
In recent years, the programming capabilities of large language models (LLMs) have garnered significant attention. Fuzz testing, a highly effective technique, plays a key role in e…
Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs
Wei Ma, Zhihao Lin, Shangqing Liu +8
Code analysis is fundamental in Software Engineering, supporting debugging, optimization, and security assessment. Human developers approach it through syntax parsing, static seman…
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search
Jie Zhang, Wei Ma, Qiang Hu +4
Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and t…
Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities
Wei Ma, Shangqing Liu, Mengjie Zhao +5
Past research has examined how well these models grasp code syntax, yet their understanding of code semantics still needs to be explored. We extensively analyze seven code models t…
GraphCode2Vec: Generic Code Embedding via Lexical and Program Dependence Analyses
Wei Ma, Mengjie Zhao, Ezekiel Soremekun +6
Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs t…
Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty
Xiyue Zhang, Xiaofei Xie, Lei Ma +5
Over the past decade, deep learning (DL) has been successfully applied to many industrial domain-specific tasks. However, the current state-of-the-art DL software still suffers fro…
An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms
Qianyu Guo, Sen Chen, Xiaofei Xie +6
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in archite…
An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms
Qianyu Guo, Xiaofei Xie, Lei Ma +6
Deep learning (DL) has recently achieved tremendous success in a variety of cutting-edge applications, e.g., image recognition, speech and natural language processing, and autonomo…
Secure Deep Learning Engineering: A Software Quality Assurance Perspective
Lei Ma, Felix Juefei-Xu, Minhui Xue +7
Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processi…