AST-Transformer: Encoding Abstract Syntax Trees Efficiently for Code Summarization
원문 초록
Code summarization aims to generate brief natural language descriptions for source code. As source code is highly structured and follows strict programming language grammars, its Abstract Syntax Tree (AST) is often leveraged to inform the encoder about the structural information. However, ASTs are usually much longer than the source code. Current approaches ignore the size limit and simply feed the whole linearized AST into the encoder. To address this problem, we propose AST-Transformer to efficiently encode tree-structured ASTs. Experiments show that AST-Transformer outperforms the state-of-arts by a substantial margin while being able to reduce $90\sim95\%$ of the computational complexity in the encoding process.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Variational Prefix Tuning for Diverse and Accurate Code Summarization Using Pre-trained Language Models
Code Summarization Beyond Function Level
How to Select Pre-Trained Code Models for Reuse? A Learning Perspective
Transducer Tuning: Efficient Model Adaptation for Software Tasks Using Code Property Graphs
Model Editing for LLMs4Code: How Far are We?
Building A Coding Assistant via the Retrieval-Augmented Language Model
토론
이 논문과 연결된 포럼 글이 아직 없습니다.