Code Structure Guided Transformer for Source Code Summarization

Understand

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance.

• Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved promising performance.
• However, effectively integrating the code structure information into the 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.