3 papers
cs.SE2026
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…
cs.SE2026
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…
cs.SE2025
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…