Code Representation Learning Using Prüfer Sequences (Student Abstract)


  • Tenzin Jinpa The University of British Columbia
  • Yong Gao The University of British Columbia



Machine Learning, Applications Of AI, Statistical Learning, Information Retrieval


An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for code representation learning. In this study, we propose to use the Prufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prufer-sequence-based representation is indeed highly effective and efficient.




How to Cite

Jinpa, T., & Gao, Y. (2022). Code Representation Learning Using Prüfer Sequences (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12977-12978.