@inproceedings{li-etal-2024-deveval,
title = "{D}ev{E}val: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories",
author = "Li, Jia and
Li, Ge and
Zhao, Yunfei and
Li, Yongmin and
Liu, Huanyu and
Zhu, Hao and
Wang, Lecheng and
Liu, Kaibo and
Fang, Zheng and
Wang, Lanshen and
Ding, Jiazheng and
Zhang, Xuanming and
Zhu, Yuqi and
Dong, Yihong and
Jin, Zhi and
Li, Binhua and
Huang, Fei and
Li, Yongbin and
Gu, Bin and
Yang, Mengfei",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.214",
pages = "3603--3614",
abstract = "How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. (1) DevEval aligns with real-world repositories in multiple dimensions, e.g., code and dependency distributions. (2) DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). (3) DevEval comprises 1,825 testing samples from 115 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs{'} coding abilities in real-world code repositories. For example, the highest Pass@1 of gpt-4 only is 53.04{\%} in our experiments. We also analyze LLMs{'} failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs{'} predictions have been released.",
}
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<abstract>How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. (1) DevEval aligns with real-world repositories in multiple dimensions, e.g., code and dependency distributions. (2) DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). (3) DevEval comprises 1,825 testing samples from 115 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs’ coding abilities in real-world code repositories. For example, the highest Pass@1 of gpt-4 only is 53.04% in our experiments. We also analyze LLMs’ failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs’ predictions have been released.</abstract>
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%0 Conference Proceedings
%T DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
%A Li, Jia
%A Li, Ge
%A Zhao, Yunfei
%A Li, Yongmin
%A Liu, Huanyu
%A Zhu, Hao
%A Wang, Lecheng
%A Liu, Kaibo
%A Fang, Zheng
%A Wang, Lanshen
%A Ding, Jiazheng
%A Zhang, Xuanming
%A Zhu, Yuqi
%A Dong, Yihong
%A Jin, Zhi
%A Li, Binhua
%A Huang, Fei
%A Li, Yongbin
%A Gu, Bin
%A Yang, Mengfei
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F li-etal-2024-deveval
%X How to evaluate the coding abilities of Large Language Models (LLMs) remains an open question. We find that existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of LLMs.To address the knowledge gap, we propose a new benchmark named DevEval, which has three advances. (1) DevEval aligns with real-world repositories in multiple dimensions, e.g., code and dependency distributions. (2) DevEval is annotated by 13 developers and contains comprehensive annotations (e.g., requirements, original repositories, reference code, and reference dependencies). (3) DevEval comprises 1,825 testing samples from 115 repositories, covering 10 popular domains (e.g., Internet, Database). Based on DevEval, we propose repository-level code generation and evaluate 8 popular LLMs on DevEval (e.g., gpt-4, gpt-3.5, StarCoder 2, DeepSeek Coder, CodeLLaMa). Our experiments reveal these LLMs’ coding abilities in real-world code repositories. For example, the highest Pass@1 of gpt-4 only is 53.04% in our experiments. We also analyze LLMs’ failed cases and summarize their shortcomings. We hope DevEval can facilitate the development of LLMs in real code repositories. DevEval, prompts, and LLMs’ predictions have been released.
%U https://aclanthology.org/2024.findings-acl.214
%P 3603-3614
Markdown (Informal)
[DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories](https://aclanthology.org/2024.findings-acl.214) (Li et al., Findings 2024)
ACL
- Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, et al.. 2024. DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories. In Findings of the Association for Computational Linguistics ACL 2024, pages 3603–3614, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.