A Multi-Label Code Comment Classifier using BERT

Code comments play an essential role in software development by providing documentation, explanations, and clarifications for program logic and functionality. It is crucial to effectively classify code comments to improve software maintainability and collaboration in the face of a growing amount of code. Developers can easily identify and comprehend different code sections' purpose, behavior, and requirements by accurately classifying code comments. This paper presents a novel approach utilizing multi-label classification to enhance code comment classification in three programming languages: Python, Pharo, and Java. We employ BERT, a widely used language model, and achieve an F1 score of 0.64 through experimentation. Our proposed approach facilitates the understanding and managing code comments, making software development more efficient and productive. Additionally, our approach can be extended to other programming languages and serve as a foundation for further research in code comment classification.