9 papers
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…
Toward a Generalized Defense Across Sparse, Continuous, and Structured Parameter Attacks
Bin Duan, Zeyu Bai, Guowei Yang
Deep neural networks are increasingly deployed across heterogeneous and partially untrusted environments, where models are distributed through cloud storage, CI/CD pipelines, conta…
Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes
Bin Duan, Meiru Che, Guowei Yang
As neural networks are increasingly deployed in safety-critical domains, testing is essential to evaluate and improve their reliability. Existing testing methods, whether black-box…
Latent Anchor-Driven Test Generation for Deep Neural Networks
Bin Duan, Matthew B. Dwyer, Guowei Yang
Deep Neural Networks (DNNs) are increasingly being deployed in security-critical and safety-sensitive applications, which makes rigorous testing essential to identify and mitigate…
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…