3 papers
cs.SE2026
LLM-Guided Issue Generation from Uncovered Code Segments
Diany Pressato, Honghao Tan, Mariam Elmoazen +1
Developers are increasingly overwhelmed by AI-generated issue reports that lack actionability and reproducibility, eroding trust in automated bug detection tools. In this paper, we…
cs.SE2025
Exploring the Jupyter Ecosystem: An Empirical Study of Bugs and Vulnerabilities
Wenyuan Jiang, Diany Pressato, Harsh Darji +1
Background. Jupyter notebooks are one of the main tools used by data scientists. Notebooks include features (configuration scripts, markdown, images, etc.) that make them challengi…
cs.SE2025
Automated Harmfulness Testing for Code Large Language Models
Honghao Tan, Haibo Wang, Diany Pressato +2
Generative AI systems powered by Large Language Models (LLMs) usually use content moderation to prevent harmful content spread. To evaluate the robustness of content moderation, se…