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2021-03-19 - Colloque/Article dans les actes avec comité de lecture - Anglais - 5 page(s)

Mens Tom , "Evaluating a bot detection model on git commit messages" in Belgium-Netherlands Software Evolution Workshop (BENEVOL), Luxembourg, Luxembourg, 2020

  • Codes CREF : Informatique appliquée logiciel (DI2570), Informatique générale (DI1162), Analyse de systèmes informatiques (DI2572)
  • Unités de recherche UMONS : Génie Logiciel (S852)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
Texte intégral :

Abstract(s) :

(Anglais) Detecting the presence of bots in distributed software development activity is very important in order to prevent bias in large-scale socio-technical empirical analyses. In previous work, we proposed a classification model to detect bots in GitHub repositories based on the pull request and issue comments of GitHub accounts. The current study generalises the approach to git contributors based on their commit messages. We train and evaluate the classification model on a large dataset of 6,922 git contributors. The original model based on pull request and issue comments obtained a precision of 0.77 on this dataset. Retraining the classification model on git commit messages increased the precision to 0.80. As a proof-of-concept, we implemented this model in BoDeGiC, an open source command-line tool to detect bots in git repositories.

Identifiants :
  • FNRS : O.0157.18F-RG43
  • FNRS : T.0017.18
  • FNRS : J.0151.20
  • FNRS : J.0151.20

Mots-clés :
  • (Anglais) open source software
  • (Anglais) GitHub
  • (Anglais) distributed software development
  • (Anglais) development bots