activity
20232026
most citedCodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2026

EpiDroid: Dependency-Guided Recomposition for Deep State Discovery in Mobile GUI Testing

Jiahui Song, Jiaxin Zhi, Kangjia Zhao +6

The increasing scale and complexity of mobile applications make automated GUI exploration essential for software quality assurance. However, existing methods often neglect state de…

cs.SE2026

CodeGlance: Understanding Code Reasoning Challenges in LLMs through Multi-Dimensional Feature Analysis

Yunkun Wang, Xuanhe Zhang, Junxiao Han +2

In modern software development, developers frequently need to understand code behavior at a glance -- whether reviewing pull requests, debugging issues, or navigating unfamiliar co…

cs.SE2025

InspectCoder: Dynamic Analysis-Enabled Self Repair through interactive LLM-Debugger Collaboration

Yunkun Wang, Yue Zhang, Guochang Li +5

Large Language Models (LLMs) frequently generate buggy code with complex logic errors that are challenging to diagnose. While existing LLM-based self-repair approaches conduct inte…

cs.SE2025★ 1 cited

CoReQA: Uncovering Potentials of Language Models in Code Repository Question Answering

Jialiang Chen, Kaifa Zhao, Jie Liu +6

Large language models that enhance software development tasks, such as code generation, code completion, and code question answering (QA), have been extensively studied in both aca…

cs.CV2024

GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing

Hao Lu, Xuesong Niu, Jiyao Wang +12

Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. Despite its success in l…

cs.CL2023★ 3 cited

CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation

Weixiang Yan, Haitian Liu, Yunkun Wang +8

Large Language Models (LLMs) have demonstrated remarkable performance on assisting humans in programming and facilitating programming automation. However, existing benchmarks for e…