@inproceedings{li-etal-2025-fea,
    title = "{FEA}-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation",
    author = "Li, Wei  and
      Zhang, Xin  and
      Guo, Zhongxin  and
      Mao, Shaoguang  and
      Luo, Wen  and
      Peng, Guangyue  and
      Huang, Yangyu  and
      Wang, Houfeng  and
      Li, Scarlett",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.839/",
    pages = "17160--17176",
    ISBN = "979-8-89176-251-0",
    abstract = "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 models (LLMs) to perform incremental development within code repositories. We collect pull requests from 83 GitHub repositories and use rule-based and intent-based filtering to construct task instances focused on new feature development. Each task instance containing code changes is paired with relevant unit test files to ensure that the solution can be verified. The feature implementation requires LLMs to simultaneously possess code completion capabilities for new components and code editing abilities for other relevant parts in the code repository, providing a more comprehensive evaluation method of LLMs' automated software engineering capabilities.Experimental results show that LLMs perform significantly worse in the FEA-Bench, highlighting considerable challenges in such repository-level incremental code development."
}