Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE) |
| Publisher | IEEE |
| Pages | 28-31 |
| Number of pages | 4 |
| ISBN (Print) | 979-8-3503-0178-6 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE) - Melbourne, Australia Duration: 14 May 2023 → 20 May 2023 Conference number: 2 https://nlbse2023.github.io/ |
Workshop
| Workshop | 2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE) |
|---|---|
| Abbreviated title | NLBSE 2023 |
| Country/Territory | Australia |
| City | Melbourne |
| Period | 14/05/23 → 20/05/23 |
| Internet address |
Bibliographical note
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-careOtherwise 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.