A syntax-guided edit decoder for neural program repair
Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, Lu Zhang
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.
Topics & Concepts
Computer scienceIdentifierEncoderProgramming languageSyntaxStatement (logic)Decoding methodsSoftwareSoft-decision decoderArtificial intelligenceOperating systemAlgorithmPolitical scienceLawSoftware Testing and Debugging TechniquesSoftware Engineering ResearchAdversarial Robustness in Machine Learning