Dopamin: Transformer-based Comment Classifiers through Domain Post-Training and Multi-level Layer Aggregation - Hai, Nam Le; Bui, Nghi D. Q. (2024 IEEE/ACM International Workshop on Natural Language-Based Software Engineering (NLBSE), 2024)
Code comments provide important information for understanding the source code. They can help developers understand the overall purpose of a function or class, as well as identify bugs and technical debt. However, an overabundance of comments is meaningless and counterproductive. As a result, it is critical to automatically filter out these comments for specific purposes. In this paper, we present Dopamin, a Transformer-based tool for dealing with this issue. Our model excels not only in presenting knowledge sharing of common categories across multiple languages, but also in achieving robust performance in comment classification by improving comment representation. As a result, it outperforms the STACC baseline by 3 % on the NLBSE'24 Tool Competition dataset in terms of average F1 score, while maintaining a comparable inference time for practical use. The source code is publicity available at https://github.com/FSoft-AI4Code/Dopamin.