UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing-Reference-Cited by-同舟云学术

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UniTSyn: A Large-Scale Dataset Capable of Enhancing the Prowess of Large Language Models for Program Testing

Published:2024-09-11 Issue: Volume:40 Page:1061-1072
ISSN:
Container-title:Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis
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Author:

He Yifeng¹^ORCID,Huang Jiabo²^ORCID,Rong Yuyang¹^ORCID,Guo Yiwen³^ORCID,Wang Ethan¹^ORCID,Chen Hao¹^ORCID

Affiliation:

1. University of California at Davis, Davis, USA

2. Tencent, Shenzhen, China

3. Unaffiliated, n.n., China

Funder

NSF (National Science Foundation)

UC Noyce Initiative

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650212.3680342

Reference90 articles.

1. Wasi Ahmad Saikat Chakraborty Baishakhi Ray and Kai-Wei Chang. 2020.

2. A transformer-based approach for source code summarization. InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

3. Association for Computational Linguistics, Online, ( July 2020 ) h. ttps://doi.org /10.18653/v1/ 2020.acl-main. 449.

4. Loubna Ben Allal Raymond Li Denis Kocetkov Chenghao Mou Christopher Akiki et al. 2023. Santacoder: don't reach for the stars !arXiv preprint arXiv: 2301. 03988.

5. Dimitrios Athanasiou Ariadi Nugroho Joost Visser and Andy Zaidman. 2014.

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