activity
20232026
most citedEmotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench

15 citations · 43 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.SE2026

SWE-Chain: Benchmarking Coding Agents on Chained Release-Level Package Upgrades

Man Ho Lam, Chaozheng Wang, Hange Liu +5

Coding agents powered by large language models are increasingly expected to perform realistic software maintenance tasks beyond isolated issue resolution. Existing benchmarks have…

cs.SE2026

ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback

Jingyu Xiao, Jiantong Qin, Shuoqi Li +5

Multimodal Large Language Models (MLLMs) have demonstrated strong performance on the UI-to-code task, which aims to generate UI code from design mock-ups. However, when applied to…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…