153 citations · 322 across the 21 of their papers we have counts for
23 papers
Efficient Function Orchestration for Large Language Models
Xiaoxia Liu, Peng Di, Cong Li +2
Function calling is a fundamental capability of today's large language models, but sequential function calling posed efficiency problems. Recent studies have proposed to request fu…
Drive: User-Controlled Autonomous Driving
Kun Wang, Christopher M. Poskitt, Yang Sun +4
Autonomous Vehicles (AVs) rely on sophisticated Autonomous Driving Systems (ADSs) to provide passengers a satisfying and safe journey. The individual preferences of riders plays a…
TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination
Mengdi Zhang, Jun Sun, Jingyi Wang +1
Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains such as face reco…
K-ST: A Formal Executable Semantics of the Structured Text Language for PLCs
Kun Wang, Jingyi Wang, Christopher M. Poskitt +3
Programmable Logic Controllers (PLCs) are responsible for automating process control in many industrial systems (e.g. in manufacturing and public infrastructure), and thus it is cr…
Repairing Adversarial Texts through Perturbation
Guoliang Dong, Jingyi Wang, Jun Sun +5
It is known that neural networks are subject to attacks through adversarial perturbations, i.e., inputs which are maliciously crafted through perturbations to induce wrong predicti…
Better Pay Attention Whilst Fuzzing
Shunkai Zhu, Jingyi Wang, Jun Sun +4
Fuzzing is one of the prevailing methods for vulnerability detection. However, even state-of-the-art fuzzing methods become ineffective after some period of time, i.e., the coverag…
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Jialuo Chen, Jingyi Wang, Tinglan Peng +6
Deep learning (DL) models, especially those large-scale and high-performance ones, can be very costly to train, demanding a great amount of data and computational resources. Unauth…
Influence of spin fluctuations on structural phase transitions of iron
Ning Wang, Thomas Hammerschmidt, Tilmann Hickel +2
The effect of spin fluctuations on the (bcc) - (fcc) - (bcc) structural phase transitions in iron is investigated with a tight-binding (TB) model. The orthogonal -va…
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
Peixin Zhang, Jingyi Wang, Jun Sun +5
Although deep learning has demonstrated astonishing performance in many applications, there are still concerns about its dependability. One desirable property of deep learning appl…
Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems
Yifan Jia, Jingyi Wang, Christopher M. Poskitt +3
The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors b…
Attack as Defense: Characterizing Adversarial Examples using Robustness
Zhe Zhao, Guangke Chen, Jingyi Wang +3
As a new programming paradigm, deep learning has expanded its application to many real-world problems. At the same time, deep learning based software are found to be vulnerable to…
RobOT: Robustness-Oriented Testing for Deep Learning Systems
Jingyi Wang, Jialuo Chen, Youcheng Sun +4
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction…
Towards Repairing Neural Networks Correctly
Guoliang Dong, Jun Sun, Jingyi Wang +2
Neural networks are increasingly applied to support decision making in safety-critical applications (like autonomous cars, unmanned aerial vehicles and face recognition based authe…
Improving Neural Network Verification through Spurious Region Guided Refinement
Pengfei Yang, Renjue Li, Jianlin Li +5
We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze…
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Yizhen Dong, Peixin Zhang, Jingyi Wang +7
Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs…
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
Guoliang Dong, Jingyi Wang, Jun Sun +5
Neural networks are becoming a popular tool for solving many real-world problems such as object recognition and machine translation, thanks to its exceptional performance as an end…
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
Jingyi Wang, Guoliang Dong, Jun Sun +2
Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a nor…
Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing
Jingyi Wang, Jun Sun, Peixin Zhang +1
Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples. Adversarial samples are often crafted through adversarial perturbati…
Toward `verifying' a Water Treatment System
Jingyi Wang, Jun Sun, Yifan Jia +2
Modeling and verifying real-world cyber-physical systems is challenging, which is especially so for complex systems where manually modeling is infeasible. In this work, we report o…
Learning Likely Invariants to Explain Why a Program Fails
Jun Sun, Long H. Pham, Lyly Tran Thi +2
Debugging is difficult. Recent studies show that automatic bug localization techniques have limited usefulness. One of the reasons is that programmers typically have to understand…
Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement
Jingyi Wang, Jun Sun, Shengchao Qin +1
Precisely modeling complex systems like cyber-physical systems is challenging, which often render model-based system verification techniques like model checking infeasible. To over…
Towards Concolic Testing for Hybrid Systems
Pingfan Kong, Yi Li, Xiaohong Chen +3
Hybrid systems exhibit both continuous and discrete behavior. Analyzing hybrid systems is known to be hard. Inspired by the idea of concolic testing (of programs), we investigate w…
Should We Learn Probabilistic Models for Model Checking? A New Approach and An Empirical Study
Jingyi Wang, Jun Sun, Qixia Yuan +1
Many automated system analysis techniques (e.g., model checking, model-based testing) rely on first obtaining a model of the system under analysis. System modeling is often done ma…