STACC: Code Comment Classification using SentenceTransformers

STACC: Code Comment Classification using SentenceTransformers

Al-Kaswan, A. (TU Delft Software Engineering)
Izadi, M. (TU Delft Software Engineering)
van Deursen, A. (TU Delft Software Technology)

Software Technology

2023

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 clas-sify 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 Fl 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.

http://resolver.tudelft.nl/uuid:fb249e73-6d68-4ed9-a7f0-dd77f59212b4

https://doi.org/10.1109/NLBSE59153.2023.00014

IEEE

2024-01-29

979-8-3503-0178-6

2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE)

2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE), 2023-05-14 → 2023-05-20, Melbourne, Australia

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2023 A. Al-Kaswan, M. Izadi, A. van Deursen