activity
20242026
most citedLLMAID: Identifying AI Capabilities in Android Apps with LLMs

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

collaborators

5 papers

cs.SE2026

Compiling Code LLMs into Lightweight Executables

Jieke Shi, Junda He, Zhou Yang +6

The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have prod…

cs.SE2026★ 1 cited

Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards

Zejun Zhang, Yixin Gan, Zhenchang Xing +5

Coding standards are essential for maintaining consistent and high-quality code across teams and projects. Linters help developers enforce these standards by detecting code violati…

cs.SE2025★ 1 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.SE2024

Automated Soap Opera Testing Directed by LLMs and Scenario Knowledge: Feasibility, Challenges, and Road Ahead

Yanqi Su, Zhenchang Xing, Chong Wang +4

Exploratory testing (ET) harnesses tester's knowledge, creativity, and experience to create varying tests that uncover unexpected bugs from the end-user's perspective. Although ET…

cs.CR2024★ 1 cited

{A New Hope}: Contextual Privacy Policies for Mobile Applications and An Approach Toward Automated Generation

Shidong Pan, Zhen Tao, Thong Hoang +7

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, th…