Zhongxin Guo — accepted AI-conference papers

2025

EpiCoder: Encompassing Diversity and Complexity in Code Generation

Yaoxiang Wang, Haoling Li, Xin Zhang, Jie Wu, Xiao Liu, Wenxiang Hu +7

ICML 2025poster

Existing methods for code generation use code snippets as seed data, restricting the complexity and diversity of the synthesized data. In this paper, we introduce a novel feature tree-based synthesis framework, which revolves around hierarchical code features derived from high-level abstractions of…

Cited by 4 Source ↗PDF ↗Scholar ↗

2025

FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation

Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng +3

ACL 2025long

Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for this capability. To fill this gap, we introduce FEA-Bench, a benchmark designed to assess the ability of large language mo…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗