Code Representation Learning Using Prüfer Sequences (Student Abstract)
Tenzin Jinpa, Yong Gao
[AAAI-22] Student Abstract and Poster Program
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.
Sessions where this paper appears
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Poster Session 1
Thu, February 24 4:45 PM - 6:30 PM (+00:00)
Blue 5
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Poster Session 11
Mon, February 28 12:45 AM - 2:30 AM (+00:00)
Blue 5