15 citations · 43 across the 11 of their papers we have counts for
5 papers · 1 filter
TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework
Shuzheng Gao, Eric John Li, Man Ho Lam +5
Large foundation models are fundamentally transforming the software engineering landscape, demonstrating exceptional capabilities across diverse tasks such as code generation, debu…
JSProtect: A Scalable Obfuscation Framework for Mini-Games in WeChat
Zhihao Li, Chaozheng Wang, Zongjie Li +12
The WeChat mini-game ecosystem faces rampant intellectual property theft to other platforms via secondary development, yet existing JavaScript obfuscation tools are ill-equipped fo…
The PIMMUR Principles: Ensuring Validity in Collective Behavior of LLM Societies
Jiaxu Zhou, Jen-tse Huang, Xuhui Zhou +5
Large language models (LLMs) are increasingly used to simulate human collective behavior, yet claims that such simulations are human-like remain largely untested. We conducted a sy…
DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation
Jingyu Xiao, Ming Wang, Man Ho Lam +4
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in automated front-end engineering, e.g., generating UI code from visual designs. However, existi…
CodeCrash: Exposing LLM Fragility to Misleading Natural Language in Code Reasoning
Man Ho Lam, Chaozheng Wang, Jen-tse Huang +1
Large Language Models (LLMs) have recently demonstrated strong capabilities in code-related tasks, but their robustness in code reasoning under perturbations remains underexplored.…