An extensive study on pre-trained models for program understanding and generation

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

107 Citations (Scopus)

Original language	English
Title of host publication	Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2022)
Publisher	Association for Computing Machinery (ACM)
Pages	39-51
DOIs	https://doi.org/10.1145/3533767.3534390
Publication status	Published - Jul 2022

More information

@inproceedings{f47b3277871042858b8d4cce12c89210,

title = "An extensive study on pre-trained models for program understanding and generation",

author = "Zhengran Zeng and Hanzhuo Tan and Haotian Zhang and Jing Li and Yuqun Zhang and Lingming Zhang",

year = "2022",

month = jul,

doi = "10.1145/3533767.3534390",

language = "English",

pages = "39--51",

booktitle = "Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2022)",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

}

An extensive study on pre-trained models for program understanding and generation. / Zeng, Zhengran; Tan, Hanzhuo; Zhang, Haotian et al.
Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2022). Association for Computing Machinery (ACM), 2022. p. 39-51.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

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AU - Tan, Hanzhuo

AU - Zhang, Haotian

AU - Li, Jing

AU - Zhang, Yuqun

AU - Zhang, Lingming

PY - 2022/7

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DO - 10.1145/3533767.3534390

M3 - Conference article published in proceeding or book

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BT - Proceedings of the 31st ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA 2022)

PB - Association for Computing Machinery (ACM)

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