STACC: Code Comment Classification using SentenceTransformers
Ali Al-Kaswan, Maliheh Izadi, Arie van Deursen
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.
Topics & Concepts
Computer scienceBaseline (sea)Code (set theory)Margin (machine learning)Replication (statistics)Key (lock)Set (abstract data type)Binary classificationArtificial intelligenceMachine learningSoftwareResource (disambiguation)Data miningSupport vector machineProgramming languageStatisticsMathematicsComputer securityComputer networkOceanographyGeologySoftware Engineering ResearchSoftware Reliability and Analysis ResearchWeb Application Security Vulnerabilities