Code Structure Guided Transformer for Source Code Summarization

• URL: http://arxiv.org/abs/2104.09340v1
• Date: Mon, 19 Apr 2021 14:26:56 GMT
• Title: Code Structure Guided Transformer for Source Code Summarization
• Authors: Shuzheng Gao, Cuiyun Gao, Yulan He, Jichuan Zeng, Lun Yiu Nie, Xin Xia
• Abstract summary: Transformer-based approaches do not explicitly incorporate the code structure information which is important for capturing code semantics. We propose a novel approach named SG-Trans to incorporate code structural properties into Transformer.
• Score: 17.512699897227055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Source code summarization aims at generating concise descriptions of given programs' functionalities. While Transformer-based approaches achieve promising performance, they do not explicitly incorporate the code structure information which is important for capturing code semantics. Besides, without explicit constraints, multi-head attentions in Transformer may suffer from attention collapse, leading to poor code representations for summarization. Effectively integrating the code structure information into Transformer is under-explored in this task domain. In this paper, we propose a novel approach named SG-Trans to incorporate code structural properties into Transformer. Specifically, to capture the hierarchical characteristics of code, we inject the local symbolic information (e.g., code tokens) and global syntactic structure (e.g., data flow) into the self-attention module as inductive bias. Extensive evaluation shows the superior performance of SG-Trans over the state-of-the-art approaches.

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