When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?-Reference-Cited by-同舟云学术

When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?

Published:2024-04-14 Issue: Volume:55 Page:459-471
ISSN:
Container-title:Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings
language:
Short-container-title:

Author:

Chen Yuxiao¹²^ORCID,Wu Jingzheng³^ORCID,Ling Xiang¹^ORCID,Li Changjiang⁴^ORCID,Rui Zhiqing¹²^ORCID,Luo Tianyue¹^ORCID,Wu Yanjun¹^ORCID

Affiliation:

1. Institute of Software, Chinese Academy of Sciences, Beijing, China

2. University of Chinese Academy of Sciences, Beijing, China

3. Institute of Software, The Chinese Academy of Sciences, Beijing, China

4. Stony Brook University, New York, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3639478.3647633

Reference37 articles.

1. Rohan Anil Andrew M Dai Orhan Firat Melvin Johnson Dmitry Lepikhin Alexandre Passos Siamak Shakeri Emanuel Taropa Paige Bailey Zhifeng Chen et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403 (2023).

2. A novel co-evolutionary approach to automatic software bug fixing

3. Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B Ashok, Shashank Shet, et al. 2023. Codeplan: Repository-level coding using llms and planning. arXiv preprint arXiv:2309.12499 (2023).

4. Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.

5. Jialun Cao, Meiziniu Li, Ming Wen, and Shing-chi Cheung. 2023. A study on prompt design, advantages and limitations of chatgpt for deep learning program repair. arXiv preprint arXiv:2304.08191 (2023).