Code Representation Learning Using Prüfer Sequences (Student 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 Prüfer 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 recentlyproposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prüfersequence-based representation is indeed highly effective and efficient.

Authors

• Tenzin Jinpa
• Yong Gao

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