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cs.SE2026
VLTest: Finding Joint Image-Text Failures in Vision-Language Models
Bin Duan, Jintao Lin, Guowei Yang
Vision-language models (VLMs) are increasingly embedded in software systems. Testing them raises two problems. Mutating either input can change the expected answer, so an output ch…
cs.SE2026
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…
cs.SE2026
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…