Proceedings:
No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 36
Track:
AAAI Student Abstract and Poster Program
Downloads:
Abstract:
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.
DOI:
10.1609/aaai.v36i11.21625
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 36