STACC: Code Comment Classification using SentenceTransformers
This work addresses the need for efficient classification of code comments in software engineering, though it is incremental as it builds on existing methods with a new model.
The paper tackled the problem of automatically classifying code comments by proposing STACC, a set of SentenceTransformers-based binary classifiers, which achieved an average F1 score of 0.74, surpassing a baseline of 0.31 by 139% on the NLBSE competition dataset.
Code comments are a key resource for information about software artefacts. Depending on the use case, only some types of comments are useful. Thus, automatic approaches to classify these comments have been proposed. In this work, we address this need by proposing, STACC, a set of SentenceTransformers-based binary classifiers. These lightweight classifiers are trained and tested on the NLBSE Code Comment Classification tool competition dataset, and surpass the baseline by a significant margin, achieving an average F1 score of 0.74 against the baseline of 0.31, which is an improvement of 139%. A replication package, as well as the models themselves, are publicly available.