1 citations · 1 across the 7 of their papers we have counts for
8 papers
EviRCA: Decoupling Evidence Extraction from Reasoning for Microservice Root-Cause Analysis
Yuhao Wang, Zhen Qin, Xingliang Wang +3
Root-cause analysis (RCA) is a critical yet labor-intensive task for maintaining modern microservice systems, making it an attractive target for large language models (LLMs). Recen…
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…
RFAmpDesigner: A Self-Evolving Multi-Agent LLM Framework for Automated Radio Frequency Amplifier Design
Hang Lu, Guochang Li, Qianyu Chen +9
Automating radio frequency (RF) amplifier design remains challenging because existing methods suffer from the curse of dimensionality, weak use of domain knowledge, and poor transf…
Can Vision-Language Models Handle Long-Context Code? An Empirical Study on Visual Compression
Jianping Zhong, Guochang Li, Chen Zhi +6
Large Language Models (LLMs) struggle with long-context code due to window limitations. Existing textual code compression methods mitigate this via selective filtering but often di…
Better Call Grep: Evaluating and Improving Grep-Like Lexical Retrieval for Repository-Level Code Completion
Baoyi Wang, Xingliang Wang, Guochang Li +6
Repository-level code completion remains challenging for large language models (LLMs) due to cross-file dependencies and limited context windows. Prior work addresses this challeng…
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…
Exploring Parameter-Efficient Fine-Tuning of Large Language Model on Automated Program Repair
Guochang Li, Chen Zhi, Jialiang Chen +2
Automated Program Repair (APR) aims to fix bugs by generating patches. And existing work has demonstrated that "pre-training and fine-tuning" paradigm enables Large Language Models…