CoChat
Sign Up
Cited by 147Software Engineering ResearchTopic ModelingSoftware Testing and Debugging Techniques

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Yu, Hao, Shen, Bo, Ran, Dezhi, Zhang, Jiaxin, Zhang, Qi, Ma, Yuchi, Liang, Guangtai, Li, Ying, Wang, Qianxiang, Xie, Tao·2024·Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
DOI 10.1145/3597503.3623316
Open this paper in CoChat — free account

Abstract

Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To evaluate the effectiveness of these models, multiple existing benchmarks (e.g., HumanEval and AiXBench) are proposed, including only cases of generating a standalone function, i.e., a function that may invoke or access only built-in functions and standard libraries. However, non-standalone functions, which typically are not included in the existing benchmarks, constitute more than 70% of the functions in popular open-source projects, and evaluating models' effectiveness on standalone functions cannot reflect these models' effectiveness on pragmatic code generation scenarios (i.e., code generation for real settings of open source or proprietary code).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3597503.3623316

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 7 references

Research works printed but not linked: 23

  1. Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie Cai Michael Terry Quoc Le and Charles Sutton. 2021. Program Synthesis with Large Language Models. arXiv:cs.PL/2108.07732
  2. Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al. 2022. MultiPL-E: A Scalable and Extensible Approach to Benchmarking Neural Code Generation. arXiv preprint arXiv:2208.08227 (2022). · arXiv
  3. Shubham Chandel, Colin B Clement, Guillermo Serrato, and Neel Sundaresan. 2022. Training and Evaluating a Jupyter Notebook Data Science Assistant. arXiv preprint arXiv:2201.12901 (2022). · arXiv
  4. Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021). · arXiv
  5. Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, Hao Yu, Li Yan, Pingyi Zhou, Xin Wang, Yuchi Ma, Ignacio Iacobacci, Yasheng Wang, Guangtai Liang, Jiansheng Wei, Xin Jiang, Qianxiang Wang, and Qun Liu. 2022. PanGu-Coder: Program Synthesis with Function-Level Language Modeling. arXiv preprint arXiv:2207.11280 (2022). · arXiv
  6. Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022. InCoder: A Generative Model for Code Infilling and Synthesis. arXiv preprint arXiv:2204.05999 (2022). · arXiv
  7. Patrick Haluptzok, Matthew Bowers, and Adam Tauman Kalai. 2022. Language Models Can Teach Themselves to Program Better. arXiv preprint arXiv:2207.14502 (2022). · arXiv
  8. Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt. 2021. Measuring Coding Challenge Competence With APPS. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks, Vol. 1.
  9. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The Curious Case of Neural Text Degeneration. arXiv preprint arXiv:1904.09751 (2019). · arXiv
  10. Srinivasan Iyer Ioannis Konstas Alvin Cheung and Luke Zettlemoyer. 2018. Mapping Language to Code in Programmatic Context. arXiv:cs.CL/1808.09588
  11. Joel Jang, Seonghyeon Ye, and Minjoon Seo. 2023. Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts. In Transfer Learning for Natural Language Processing Workshop. 52--62.
  12. Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen tau Yih, Daniel Fried, Sida Wang, and Tao Yu. 2022. DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. arXiv:cs.SE/2211.11501
  13. Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al. 2023. StarCoder: May the Source Be with You! arXiv preprint arXiv:2305.06161 (2023). · arXiv
  14. Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023. WizardCoder: Empowering Code Large Language Models with Evol-Instruct. arXiv preprint arXiv:2306.08568 (2023). · arXiv
  15. 10.48550/arXiv.1910.13461 · DOI · Paper page
  16. Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022. A Conversational Paradigm for Program Synthesis. arXiv preprint arXiv:2203.13474 (2022). · arXiv
  17. John Schulman, Barret Zoph, Christina Kim, Jacob Hilton, Jacob Menick, Jiayi Weng, Juan Felipe Ceron Uribe, Liam Fedus, Luke Metz, Michael Pokorny, et al. 2022. ChatGPT: Optimizing Language Models for Dialogue. OpenAI blog (2022).
  18. Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow. 2022. Repository-level Prompt Generation for Large Language Models of Code. arXiv preprint arXiv:2206.12839 (2022). · arXiv
  19. Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, and Colin Raffel. 2022. What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? arXiv preprint arXiv:2204.05832 (2022). · arXiv
  20. Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig. 2022. Execution-Based Evaluation for Open-Domain Code Generation. arXiv preprint arXiv:2212.10481 (2022). · arXiv
  21. Wei Zeng, Xiaozhe Ren, Teng Su, Hui Wang, Yi Liao, Zhiwei Wang, Xin Jiang, ZhenZhang Yang, Kaisheng Wang, Xiaoda Zhang, Chen Li, Ziyan Gong, Yifan Yao, Xinjing Huang, Jun Wang, Jianfeng Yu, Qi Guo, Yue Yu, Yan Zhang, Jin Wang, Hengtao Tao, Dasen Yan, Zexuan Yi, Fang Peng, Fangqing Jiang, Han Zhang, Lingfeng Deng, Yehong Zhang, Zhe Lin, Chao Zhang, Shaojie Zhang, Mingyue Guo, Shanzhi Gu, Gaojun Fan, Yaowei Wang, Xuefeng Jin, Qun Liu, and Yonghong Tian. 2021. PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation. CoRR abs/2104.12369 (2021). arXiv:2104.12369 https://arxiv.org/abs/2104.12369 · arXiv
  22. Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022. Learning to Prompt for Vision-language Models. International Journal of Computer Vision (2022), 2337--2348.
  23. Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022. Large Language Lodels Are Human-level Prompt Engineers. arXiv preprint arXiv:2211.01910 (2022). · arXiv

Cited by

Sorted by citations

Items 101–147 of 147

Related

See all

Metadata

Authors
Yu, H., Shen, B., Ran, D., Zhang, J., Zhang, Q., Ma, Y., Liang, G., Li, Y., Wang, Q., & Xie, T.
Affiliations
  • Peking University (CN)
  • Huawei Technologies (China) (CN)
ORCID
Venue
Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
Publisher
ACM
Published
2024-02-06
Pages
1-12
ISSN
0270-5257 (ISSN-L)
DOI
10.1145/3597503.3623316
OpenAlex
W4391558462
Cited by
140
References
7
Citations by year
2026: 42 · 2025: 69 · 2024: 28
FWCI
52.93
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) (publishedVersion)
Funders
  • Natural Science Foundation of China (No.62161146003)
  • National key research and development program Project (2021YFF0704202)
  • Tencent Foundation/XPLORER PRIZE
Topics
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords
code generation benchmarks, HumanEval, codices, CodeGen, open-source code

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review