5 citations · 6 across the 9 of their papers we have counts for
12 papers
Stateful Testing for Deep Learning Compilers
Jiatao Ge, Ruixiang Qian, Yibo Hu +5
Modern deep learning compilers compile models lazily and cache artifacts across invocations. Retained compiler state can make a later invocation incorrect even when it is correct i…
MultiFixer: A Coordinator-Proposer Based Multi-Agent Framework For Fixing Multi-Hunk Bugs
Haichuan Hu, Chunrong Fang, Ye Shang +5
Automated Program Repair (APR) has benefited greatly from Large Language Models (LLMs), but existing LLM-based APR methods still struggle with multi-hunk bugs that require coordina…
Rise From The Ashes: LLM-based Static Analysis for Deep Learning Framework Bugs
Shaoyu Yang, Haifeng Lin, Chunrong Fang +6
Deep learning (DL) frameworks are critical AI infrastructures that often hide bugs with serious security implications. While dynamic approaches such as fuzzing are effective in unc…
SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction
Jiawei He, Mengyu Shi, Jiawei Liu +6
Joint Entity and Relation Extraction (JERE) is highly sensitive to training data quality, making data augmentation a natural way to improve generalization. However, existing augmen…
Learning from Repair Experience: Self-Evolving Agents for Automated Vulnerability Repair
Haichuan Hu, Guoqing Xie, Quanjun Zhang +5
Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack of intra-vulnerability experie…
SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?
Hwiwon Lee, Jiawei Liu, Dongjun Kim +4
Finding a real vulnerability in complicated systems is a challenging, long-horizon task that demands reasoning across an entire codebase to produce a working proof-of-concept (PoC)…
ATTest: Agent-Driven Tensor Testing for Deep Learning Library Modules
Zhengyu Zhan, Ye Shang, Jiawei Liu +3
The unit testing of Deep Learning (DL) libraries is challenging due to complex numerical semantics and implicit tensor constraints. Traditional Search-Based Software Testing (SBST)…
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…
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…
Mutation-Based Deep Learning Framework Testing Method in JavaScript Environment
Yinglong Zou, Juan Zhai, Chunrong Fang +3
In recent years, Deep Learning (DL) applications in JavaScript environment have become increasingly popular. As the infrastructure for DL applications, JavaScript DL frameworks pla…
Machine Translation Testing via Syntactic Tree Pruning
Quanjun Zhang, Juan Zhai, Chunrong Fang +4
Machine translation systems have been widely adopted in our daily life, making life easier and more convenient. Unfortunately, erroneous translations may result in severe consequen…
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
Jiayang Song, Zhehua Zhou, Jiawei Liu +3
Although Deep Reinforcement Learning (DRL) has achieved notable success in numerous robotic applications, designing a high-performing reward function remains a challenging task tha…