Author

Tarek Mahmud

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Preprint Oct 2026

Beyond Functional Correctness: Static Code Quality Profiling of LLM-Generated Code

Large language models (LLMs) are increasingly used for code generation, yet most evaluations still focus on whether generated code passes benchmark test suites. Functional correctness alone, however, provides only a limited view of quality: benchmark tests are often incomplete, and passing them does not guarantee that...

Jue-Zhe Huang, Tarek Mahmud, Mei-Ru Che et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Beyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization

Automatic prompt optimization (APO) uses feedback from model responses to improve prompts automatically. However, its effectiveness is usually measured through overall scores, which do not reveal which questions become correct, which previously correct answers become wrong, or whether improvements extend to unseen ques...

Chinmoy Mitra, Tarek Mahmud · 0 citations

Preprint Aug 2026

Harnessing LLMs for Document-Guided Fuzzing of Python Libraries

VistaFuzz is introduced, a document-guided fuzzing technique that uses a locally served open-sourced LLM to extract parameter specifications from API documents and generate inputs that satisfy both parameter constraints and inter-parameter dependencies.

Bin Duan, Tarek Mahmud, Meiru Che et al. · 0 citations