31 citations · 168 across the 75 of their papers we have counts for
18 papers · 1 filter
Debiasing LLMs by Masking Unfairness-Driving Attention Heads
Tingxu Han, Wei Song, Ziqi Ding +6
Large language models (LLMs) increasingly mediate decisions in domains where unfair treatment of demographic groups is unacceptable. Existing work probes when biased outputs appear…
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…
GPU Temperature Simulation-Based Testing for In-Vehicle Deep Learning Frameworks
Yinglong Zou, Juan Zhai, Chunrong Fang +1
Deep learning models play a vital role in autonomous driving systems, supporting critical functions such as environmental perception. To accelerate model inference, these deep lear…
Deep Learning Framework Testing via Model Mutation: How Far Are We?
Yanzhou Mu, Rong Wang, Juan Zhai +7
Deep Learning (DL) frameworks are a fundamental component of DL development. Therefore, the detection of DL framework defects is important and challenging. As one of the most widel…
DevMuT: Testing Deep Learning Framework via Developer Expertise-Based Mutation
Yanzhou Mu, Juan Zhai, Chunrong Fang +6
Deep learning (DL) frameworks are the fundamental infrastructure for various DL applications. Framework defects can profoundly cause disastrous accidents, thus requiring sufficient…
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…
Deep Learning Framework Testing via Heuristic Guidance Based on Multiple Model Measurements
Yinglong Zou, Juan Zhai, Chunrong Fang +3
Deep learning frameworks serve as the foundation for developing and deploying deep learning applications. To enhance the quality of deep learning frameworks, researchers have propo…
Large Language Models for Unit Testing: A Systematic Literature Review
Quanjun Zhang, Chunrong Fang, Siqi Gu +3
Unit testing is a fundamental practice in modern software engineering, with the aim of ensuring the correctness, maintainability, and reliability of individual software components.…
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…
May the Feedback Be with You! Unlocking the Power of Feedback-Driven Deep Learning Framework Fuzzing via LLMs
Shaoyu Yang, Chunrong Fang, Haifeng Lin +3
Deep Learning (DL) frameworks have served as fundamental components in DL systems over the last decade. However, bugs in DL frameworks could lead to catastrophic consequences in cr…
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…
Scenario-Guided LLM-based Mobile App GUI Testing
Shengcheng Yu, Yuchen Ling, Chunrong Fang +5
The assurance of mobile app GUI has become increasingly important, as the GUI serves as the primary medium of interaction between users and apps. Although numerous automated GUI te…
DeCoMa: Detecting and Purifying Code Dataset Watermarks through Dual Channel Code Abstraction
Yuan Xiao, Yuchen Chen, Shiqing Ma +7
Watermarking is a technique to help identify the source of data points, which can be used to help prevent the misuse of protected datasets. Existing methods on code watermarking, l…
Improving Retrieval-Augmented Deep Assertion Generation via Joint Training
Quanjun Zhang, Chunrong Fang, Yi Zheng +7
Unit testing attempts to validate the correctness of basic units of the software system under test and has a crucial role in software development and testing. Very recent work prop…
Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models
Quanjun Zhang, Chunrong Fang, Yi Zheng +7
Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual…
Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness
Weisong Sun, Yuchen Chen, Mengzhe Yuan +6
Neural code models (NCMs) have demonstrated extraordinary capabilities in code intelligence tasks. Meanwhile, the security of NCMs and NCMs-based systems has garnered increasing at…
Continuous Concepts Removal in Text-to-image Diffusion Models
Tingxu Han, Weisong Sun, Yanrong Hu +6
Text-to-image diffusion models have shown an impressive ability to generate high-quality images from input textual descriptions. However, concerns have been raised about the potent…