9 citations · 30 across the 26 of their papers we have counts for
7 papers · 1 filter
Knowledge-Guided Multi-Agent Framework for Application-Level Software Code Generation
Qian Xiong, Bo Yang, Weisong Sun +4
Automated code generation driven by Large Lan- guage Models (LLMs) has enhanced development efficiency, yet generating complex application-level software code remains challenging.…
Rethinking Technology Stack Selection with AI Coding Proficiency
Xiaoyu Zhang, Weipeng Jiang, Shiqing Ma +5
Large language models (LLMs) are now an integral part of software development workflows and are reshaping the whole process. However, existing technology selection methods mainly f…
Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol
Wei Ma, Yixiao Yang, Qiang Hu +8
Applications of Large Language Models~(LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and mult…
Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?
Zhenpeng Chen, Xinyue Li, Jie M. Zhang +5
Fairness is a critical requirement for Machine Learning (ML) software, driving the development of numerous bias mitigation methods. Previous research has identified a leveling-down…
Fair-PP: A Synthetic Dataset for Aligning LLM with Personalized Preferences of Social Equity
Qi Zhou, Jie Zhang, Dongxia Wang +5
Human preference plays a crucial role in the refinement of large language models (LLMs). However, collecting human preference feedback is costly and most existing datasets neglect…
Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents
Kaixin Wang, Tianlin Li, Xiaoyu Zhang +7
Code large language models (CodeLLMs) and agents are increasingly being integrated into complex software engineering tasks spanning the entire Software Development Life Cycle (SDLC…
A Vision for Auto Research with LLM Agents
Chengwei Liu, Chong Wang, Jiayue Cao +16
This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research. Levera…