6 papers · 1 filter
Testing Deep Learning Library APIs via Cross-Framework Differential Fuzzing
Bin Duan, Ruican Dong, Naipeng Dong +2
Deep learning libraries underpin many safety- and reliability-critical applications, yet existing API-level testing techniques often rely on intra-library properties or CPU--GPU di…
Harnessing LLMs for Document-Guided Fuzzing of Python Libraries
Bin Duan, Tarek Mahmud, Meiru Che +4
Python libraries underpin deep learning, scientific computing, data analysis, and computer vision, making their reliability critical to downstream applications. Testing their APIs…
XAMT: Cross-Framework API Matching for Testing Deep Learning Libraries
Bin Duan, Ruican Dong, Naipeng Dong +2
Deep learning powers critical applications such as autonomous driving, healthcare, and finance, where the correctness of underlying libraries is essential. Bugs in widely used deep…
Harnessing LLMs for Document-Guided Fuzzing of OpenCV Library
Bin Duan, Tarek Mahmud, Meiru Che +4
The combination of computer vision and artificial intelligence is fundamentally transforming a broad spectrum of industries by enabling machines to interpret and act upon visual da…
Enhancing LLM Code Generation with Ensembles: A Similarity-Based Selection Approach
Tarek Mahmud, Bin Duan, Corina Pasareanu +1
Ensemble learning has been widely used in machine learning to improve model robustness, accuracy, and generalization, but has not yet been applied to code generation tasks with lar…
Automated Update of Android Deprecated API Usages with Large Language Models
Tarek Mahmud, Bin Duan, Meiru Che +3
Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporate new fea…