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FSE 2021

A syntax-guided edit decoder for neural program repair

Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, Lu Zhang

PDFDOI 10.1145/3468264.3468544dblp

Abstract

Automated Program Repair (APR) helps improve the efficiency of software development and maintenance. Recent APR techniques use deep learning, particularly the encoder-decoder architecture, to generate patches. Though existing DL-based APR approaches have proposed different encoder architectures, the decoder remains to be the standard one, which generates a sequence of tokens one by one to replace the faulty statement. This decoder has multiple limitations: 1) allowing to generate syntactically incorrect programs, 2) inefficiently representing small edits, and 3) not being able to generate project-specific identifiers.

BibTeX
@inproceedings{Zhu-al:FSE21,
  author    = {Qihao Zhu and
               Zeyu Sun and
               Yuan{-}an Xiao and
               Wenjie Zhang and
               Kang Yuan and
               Yingfei Xiong and
               Lu Zhang},
  title     = {A syntax-guided edit decoder for neural program repair},
  booktitle = {{ESEC/SIGSOFT} {FSE}},
  pages     = {341--353},
  publisher = {{ACM}},
  year      = {2021},
}

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