SICUP: A Comment Updating Approach based on Structural Information

Shifan Liu, Zhanqi Cui, Ruilin Xie

Abstract: High quality code comments are of great value for program maintenance. However, during the development process, developers often neglect to update corresponding comments when changing code. In such case, inconsistent comments are introduced which affect the maintainability of the software. In previous work, code changes are usually performed by treating the code as ordinary text and the structural information of the code are ignored. In this paper, we propose an approach named SICUP (Structural Information based Comment UPdater) to provide a new solution for comment updating tasks. SICUP uses the structural information of the code to help updating comments by constructing different sequences of ASTs. Experiments on a popular dataset demonstrates that SICUP outperforms CUP, which is an effective deep learning-based approach in terms of accuracy and recall.

External IDs:dblp:conf/wcre/LiuCX23