5 papers
Harness-Bench: Measuring Harness Effects across Models in Realistic Agent Workflows
Yilun Yao, Xinyu Tan, Chao-Hsuan Liu +9
LLM agents are increasingly deployed as executable systems that use tools, modify workspaces, and produce concrete artifacts. In such workflows, performance depends not only on the…
DynaFix: Iterative Automated Program Repair Driven by Execution-Level Dynamic Information
Zhili Huang, Ling Xu, Chao Liu +5
Automated Program Repair (APR) aims to automatically generate correct patches for buggy programs. Recent approaches leveraging large language models (LLMs) have shown promise but f…
Toward Functional and Non-Functional Evaluation of Application-Level Code Generation
Ruwei Pan, Yakun Zhang, Qingyuan Liang +4
Large language models (LLMs) have achieved strong performance on code generation. However, most prior evaluations focus on snippet-level outputs, such as function generation or rep…
AdaCoder: An Adaptive Planning and Multi-Agent Framework for Function-Level Code Generation
Yueheng Zhu, Chao Liu, Xuan He +4
Recently, researchers have proposed many multi-agent frameworks for function-level code generation, which aim to improve software development productivity by automatically generati…
CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation
Ruwei Pan, Hongyu Zhang, Chao Liu
Code generation aims to produce code that fulfills requirements written in natural language automatically. Large Language Models (LLMs) like ChatGPT have demonstrated promising eff…