180 citations · 951 across the 62 of their papers we have counts for
18 papers · 1 filter
MORTAR: A Model-based Runtime Action Repair Framework for AI-enabled Cyber-Physical Systems
Renzhi Wang, Zhehua Zhou, Jiayang Song +3
Cyber-Physical Systems (CPSs) are increasingly prevalent across various industrial and daily-life domains, with applications ranging from robotic operations to autonomous driving.…
Evaluation and Improvement of Fault Detection for Large Language Models
Qiang Hu, Jin Wen, Maxime Cordy +4
Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunatel…
BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems
Mingfei Cheng, Yuan Zhou, Xiaofei Xie
Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety v…
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), espec…
Evaluating AIGC Detectors on Code Content
Jian Wang, Shangqing Liu, Xiaofei Xie +1
Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces hig…
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie +2
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…
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…
TransRepair: Context-aware Program Repair for Compilation Errors
Xueyang Li, Shangqing Liu, Ruitao Feng +4
Automatically fixing compilation errors can greatly raise the productivity of software development, by guiding the novice or AI programmers to write and debug code. Recently, learn…
Towards Understanding the Faults of JavaScript-Based Deep Learning Systems
Lili Quan, Qianyu Guo, Xiaofei Xie +3
Quality assurance is of great importance for deep learning (DL) systems, especially when they are applied in safety-critical applications. While quality issues of native DL applica…
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Deep learning (DL) plays a more and more important role in our daily life due to its competitive performance in industrial application domains. As the core of DL-enabled systems, d…
CodeS: Towards Code Model Generalization Under Distribution Shift
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming…
Learning Program Semantics with Code Representations: An Empirical Study
Jing Kai Siow, Shangqing Liu, Xiaofei Xie +2
Program semantics learning is the core and fundamental for various code intelligent tasks e.g., vulnerability detection, clone detection. A considerable amount of existing works pr…
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…
GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search
Shangqing Liu, Xiaofei Xie, Jingkai Siow +3
Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs su…
Automatic Web Testing using Curiosity-Driven Reinforcement Learning
Yan Zheng, Yi Liu, Xiaofei Xie +4
Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still heavily relies on manual efforts while automated web testing is far from achi…
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…
DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems
Xiaoning Du, Xiaofei Xie, Yi Li +3
Deep learning (DL) defines a data-driven programming paradigm that automatically composes the system decision logic from the training data. In company with the data explosion and h…
DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing
Xiaofei Xie, Lei Ma, Felix Juefei-Xu +7
In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many n…