2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…