7 papers · 1 filter
Beyond Functional Correctness: Static Code Quality Profiling of LLM-Generated Code
Jue Huang, Tarek Mahmud, Meiru Che +1
Large language models (LLMs) are increasingly used for code generation, yet most evaluations still focus on whether generated code passes benchmark test suites. Functional correctn…
A Systematic Study of Deprecated Android API Usage Updates
Tarek Mahmud, Meiru Che, Guowei Yang
Android Application Programming Interfaces (APIs) continuously evolve, causing existing APIs to become deprecated and requiring developers to migrate their usages to recommended re…
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…
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…