most citedNavigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models

1 citations · 3 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SE20251 cited

LLMAID: Identifying AI Capabilities in Android Apps with LLMs

Pei Liu, Terry Zhuo, Jiawei Deng +7

Recent advancements in artificial intelligence (AI) and its widespread integration into mobile software applications have received significant attention, highlighting the growing p…

cs.CR20251 cited

A Longitudinal Measurement of Privacy Policy Evolution for Large Language Models

Zhen Tao, Shidong Pan, Zhenchang Xing +3

Large language model (LLM) services have been rapidly integrated into people's daily lives as chatbots and agentic systems. They are nourished by collecting rich streams of data, r…

cs.SE2025

When AI Takes the Wheel: Security Analysis of Framework-Constrained Program Generation

Yue Liu, Zhenchang Xing, Shidong Pan +1

In recent years, the AI wave has grown rapidly in software development. Even novice developers can now design and generate complex framework-constrained software systems based on t…

cs.SE20251 cited

Navigating the Labyrinth: Path-Sensitive Unit Test Generation with Large Language Models

Dianshu Liao, Xin Yin, Shidong Pan +3

Unit testing is essential for software quality assurance, yet writing and maintaining tests remains time-consuming and error-prone. To address this challenge, researchers have prop…

cs.CR2025

Towards Context-aware Mobile Privacy Notice: Implementation of A Deployable Contextual Privacy Policies Generator

Haochen Gong, Zhen Tao, Shidong Pan +2

Lengthy and legally phrased privacy policies impede users' understanding of how mobile applications collect and process personal data. Prior work proposed Contextual Privacy Polici…

cs.SE2025

Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation

Tianyi Zhang, Shidong Pan, Zejun Zhang +2

Infrastructure-as-Code (IaC) generation holds significant promise for automating cloud infrastructure provisioning. Recent advances in Large Language Models (LLMs) present a promis…