6 papers
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…
Personalized Fall Detection by Balancing Data with Selective Feedback Using Contrastive Learning
Awatif Yasmin, Tarek Mahmud, Sana Alamgeer +1
Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-wo…
CONCUR: Benchmarking LLMs for Concurrent Code Generation
Jue Huang, Tarek Mahmud, Corina Pasareanu +1
Leveraging Large Language Models (LLMs) for code generation has increasingly emerged as a common practice in the domain of software engineering. Relevant benchmarks have been estab…
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…