375 citations · 1.5k across the 26 of their papers we have counts for
9 papers · 1 filter
Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng +2
Detecting bugs in Deep Learning (DL) libraries (e.g., TensorFlow/PyTorch) is critical for almost all downstream DL systems in ensuring effectiveness/safety for end users. Meanwhile…
Practical Program Repair in the Era of Large Pre-trained Language Models
Chunqiu Steven Xia, Yuxiang Wei, Lingming Zhang
Automated Program Repair (APR) aims to help developers automatically patch software bugs. However, current state-of-the-art traditional and learning-based APR techniques face the p…
Fuzzing Deep-Learning Libraries via Automated Relational API Inference
Yinlin Deng, Chenyuan Yang, Anjiang Wei +1
A growing body of research has been dedicated to DL model testing. However, there is still limited work on testing DL libraries, which serve as the foundations for building, traini…
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
Jiawei Liu, Jinkun Lin, Fabian Ruffy +4
Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to optimize deep neural network (DNN) models to meet performance, resource utilization and other r…
Less Training, More Repairing Please: Revisiting Automated Program Repair via Zero-shot Learning
Chunqiu Steven Xia, Lingming Zhang
Due to the promising future of Automated Program Repair (APR), researchers have proposed various APR techniques, including heuristic-based, template-based, and constraint-based tec…
Attention: Not Just Another Dataset for Patch-Correctness Checking
Jun Yang, Yuehan Wang, Yiling Lou +2
Automated Program Repair (APR) techniques have drawn wide attention from both academia and industry. Meanwhile, one main limitation with the current state-of-the-art APR tools is t…
Program Repair: Automated vs. Manual
Quanjun Zhang, Yuan Zhao, Weisong Sun +3
Various automated program repair (APR) techniques have been proposed to fix bugs automatically in the last decade. Although recent researches have made significant progress on the…
Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source
Anjiang Wei, Yinlin Deng, Chenyuan Yang +1
Deep learning (DL) systems can make our life much easier, and thus are gaining more and more attention from both academia and industry. Meanwhile, bugs in DL systems can be disastr…
Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation
Jiawei Liu, Yuxiang Wei, Sen Yang +2
In the past decade, Deep Learning (DL) systems have been widely deployed in various domains to facilitate our daily life. Meanwhile, it is extremely challenging to ensure the corre…