STACC: Code Comment Classification using SentenceTransformers

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
20 Downloads (Pure)

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 languageEnglish
Title of host publication2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE)
PublisherIEEE
Pages28-31
Number of pages4
ISBN (Print)979-8-3503-0178-6
DOIs
Publication statusPublished - 2023
Event2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE) - Melbourne, Australia
Duration: 14 May 202320 May 2023
Conference number: 2
https://nlbse2023.github.io/

Workshop

Workshop2023 IEEE/ACM 2nd International Workshop on Natural Language-Based Software Engineering (NLBSE)
Abbreviated titleNLBSE 2023
Country/TerritoryAustralia
CityMelbourne
Period14/05/2320/05/23
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-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.

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