Can pre-trained code embeddings improve model performance? Revisiting the use of code embeddings in software engineering tasks

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Published inEmpirical software engineering : an international journal Vol. 27; no. 3
Main Authors Ding, Zishuo, Li, Heng, Shang, Weiyi, Chen, Tse-Hsun Peter
Format Journal Article
LanguageEnglish
Published 01.05.2022
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ArticleNumber 63
Author Ding, Zishuo
Chen, Tse-Hsun Peter
Li, Heng
Shang, Weiyi
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  publication-title: PACMPL
  doi: 10.1145/3276517
  contributor:
    fullname: M Pradel
– volume: 23
  start-page: 2655
  issue: 5
  year: 2018
  ident: 10118_CR32
  publication-title: Empir Softw Eng
  doi: 10.1007/s10664-018-9595-8
  contributor:
    fullname: H Li
– start-page: 542
  volume-title: Proceedings of the 15th international conference on mining software repositories, MSR 2018, Gothenburg, Sweden, May 28–29, 2018
  year: 2018
  ident: 10118_CR53
  doi: 10.1145/3196398.3196431
  contributor:
    fullname: M Tufano
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Title Can pre-trained code embeddings improve model performance? Revisiting the use of code embeddings in software engineering tasks
Volume 27
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