Code Representation Learning with Pr\"ufer Sequences

• URL: http://arxiv.org/abs/2111.07263v1
• Date: Sun, 14 Nov 2021 07:27:38 GMT
• Title: Code Representation Learning with Pr\"ufer Sequences
• Authors: Tenzin Jinpa and Yong Gao
• Abstract summary: An effective encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models. We propose to use the Pr"ufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme. 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.
• Score: 2.2463154358632464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 tasks in computer program comprehension, such as automated code summarization and documentation. A significant challenge is to find a sequential representation that captures the structural/syntactic information in a computer program and facilitates the training of the learning models. In this paper, we propose to use the Pr\"ufer 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. Empirical studies on real-world benchmark datasets, using a sequence-to-sequence learning model we designed for code summarization, show that our Pr\"ufer-sequence-based representation is indeed highly effective and efficient, outperforming significantly all the recently-proposed deep-learning models we used as the baseline models.

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