3 citations · 5 across the 7 of their papers we have counts for
7 papers
Service Ecosystem Evolution: A Comprehensive Survey from Complex Network Perspectives
Shuiguang Deng, Xingliang Wang, Guochang Li +5
Digital society increasingly relies on complex service ecosystems, formed by interconnected services from technology giants. However, the growing scale and intricate dependencies o…
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…
Empowering RepoQA-Agent based on Reinforcement Learning Driven by Monte-carlo Tree Search
Guochang Li, Yuchen Liu, Zhen Qin +7
Repository-level software engineering tasks require large language models (LLMs) to efficiently navigate and extract information from complex codebases through multi-turn tool inte…
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…
Do Code LLMs Understand Design Patterns?
Zhenyu Pan, Xuefeng Song, Yunkun Wang +4
Code Large Language Models (LLMs) demonstrate great versatility in adapting to various downstream tasks, including code generation and completion, as well as bug detection and fixi…
ExploraCoder: Advancing code generation for multiple unseen APIs via planning and chained exploration
Yunkun Wang, Yue Zhang, Zhen Qin +5
Large language models face intrinsic limitations in coding with APIs that are unseen in their training corpora. As libraries continuously evolve, it becomes impractical to exhausti…
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…