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.

本文提出了一个名为SG-Trans的新颖方法，通过将本地符号信息和全局句法结构注入到Transformer的自注意模块作为归纳偏置，并设计能够分布在Transformer的较低层和高层的注意头中，进一步捕捉代码的层次特征，将其有效地集成到Transformer来实现深度学习自动生成代码摘要，经过广泛评估，SG-Trans相对于最佳基准测试结果在两个基准数据集上分别提高了1.4％和2.0％的METEOR得分，这是一种广泛用于测量生成质量的指标。

代码结构指导的源代码摘要变压器