42 citations · 147 across the 7 of their papers we have counts for
6 papers · 1 filter
Demystifying the Silence of Correctness Bugs in PyTorch Compiler
Meiziniu Li, Dongze Li, Jianmeng Liu +1
Performance optimization of AI infrastructure is key to the fast adoption of large language models (LLMs). The PyTorch compiler (torch.compile), a core optimization tool for deep l…
Enhancing Differential Testing With LLMs For Testing Deep Learning Libraries
Meiziniu Li, Dongze Li, Jianmeng Liu +3
Differential testing offers a promising strategy to alleviate the test oracle problem by comparing the test results between alternative implementations. However, existing different…
A study on Prompt Design, Advantages and Limitations of ChatGPT for Deep Learning Program Repair
Jialun Cao, Meiziniu Li, Ming Wen +1
ChatGPT has revolutionized many research and industrial fields. ChatGPT has shown great potential in software engineering to boost various traditional tasks such as program repair,…
COMET: Coverage-guided Model Generation For Deep Learning Library Testing
Meiziniu Li, Jialun Cao, Yongqiang Tian +3
Recent deep learning (DL) applications are mostly built on top of DL libraries. The quality assurance of these libraries is critical to the dependable deployment of DL applications…
DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs
Jialun Cao, Meiziniu Li, Xiao Chen +4
As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious ne…
SemMT: A Semantic-based Testing Approach for Machine Translation Systems
Jialun Cao, Meiziniu Li, Yeting Li +2
Machine translation has wide applications in daily life. In mission-critical applications such as translating official documents, incorrect translation can have unpleasant or somet…