16 citations · 34 across the 11 of their papers we have counts for
15 papers
Provisioning Robust and Efficient LiDAR Perception for Autonomous Driving
Tingxu Han, Zijie Shen, An Guo +4
LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency and memory r…
Securing Code Understanding: Detecting Natural Backdoor Vulnerability in Code Language Models
Yuchen Chen, Weisong Sun, Haocheng Huang +11
Code Language Models (CodeLMs) have become integral to software engineering, significantly advancing code intelligence tasks. However, their widespread adoption has raised critical…
Scalpel: Automotive Deep Learning Framework Testing via Assembling Model Components
Yinglong Zou, Juan Zhai, Chunrong Fang +3
Deep learning (DL) plays a key role in autonomous driving systems. DL models support perception modules, equipped with tasks such as object detection and sensor fusion. These DL mo…
When Autonomous Vehicle Meets V2X Cooperative Perception: How Far Are We?
An Guo, Shuoxiao Zhang, Enyi Tang +7
With the tremendous advancement of deep learning and communication technology, Vehicle-to-Everything (V2X) cooperative perception has the potential to address limitations in sensin…
Improving Deep Learning Framework Testing with Model-Level Metamorphic Testing
Yanzhou Mu, Juan Zhai, Chunrong Fang +6
Deep learning (DL) frameworks are essential to DL-based software systems, and framework bugs may lead to substantial disasters, thus requiring effective testing. Researchers adopt…
Generate Realistic Test Scenes for V2X Communication Systems
An Guo, Xinyu Gao, Chunrong Fang +6
Accurately perceiving complex driving environments is essential for ensuring the safe operation of autonomous vehicles. With the tremendous progress in deep learning and communicat…
Understanding LLM-Centric Challenges for Deep Learning Frameworks: An Empirical Analysis
Yanzhou Mu, Rong Wang, Juan Zhai +7
Large language models (LLMs) have driven significant progress across a wide range of real-world applications. Realizing such models requires substantial system-level support. Deep…
Testing the Fault-Tolerance of Multi-Sensor Fusion Perception in Autonomous Driving Systems
Haoxiang Tian, Wenqiang Ding, Xingshuo Han +5
High-level Autonomous Driving Systems (ADSs), such as Google Waymo and Baidu Apollo, typically rely on multi-sensor fusion (MSF) based approaches to perceive their surroundings. Th…
An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems
Haoxiang Tian, Xingshuo Han, Guoquan Wu +5
Multi-objective evolutionary algorithms (MOEAs) are widely used for searching optimal solutions in complex multi-component applications. Traditional MOEAs for multi-component deep…
SoVAR: Building Generalizable Scenarios from Accident Reports for Autonomous Driving Testing
An Guo, Yuan Zhou, Haoxiang Tian +7
Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal acci…
CooTest: An Automated Testing Approach for V2X Communication Systems
An Guo, Xinyu Gao, Zhenyu Chen +5
Perceiving the complex driving environment precisely is crucial to the safe operation of autonomous vehicles. With the tremendous advancement of deep learning and communication tec…
Eliminating Backdoors in Neural Code Models for Secure Code Understanding
Weisong Sun, Yuchen Chen, Chunrong Fang +7
Neural code models (NCMs) have been widely used to address various code understanding tasks, such as defect detection. However, numerous recent studies reveal that such models are…
LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos
Haoxiang Tian, Xingshuo Han, Yuan Zhou +6
Safety testing serves as the fundamental pillar for the development of autonomous driving systems (ADSs). To ensure the safety of ADSs, it is paramount to generate a diverse range…
Pre-trained Model-based Actionable Warning Identification: A Feasibility Study
Xiuting Ge, Chunrong Fang, Quanjun Zhang +8
Actionable Warning Identification (AWI) plays a pivotal role in improving the usability of static code analyzers. Currently, Machine Learning (ML)-based AWI approaches, which mainl…
Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
Weisong Sun, Chunrong Fang, Yun Miao +8
Programming language understanding and representation (a.k.a code representation learning) has always been a hot and challenging task in software engineering. It aims to apply deep…