DI-UMONS : Dépôt institutionnel de l’université de Mons

Recherche transversale
(titres de publication, de périodique et noms de colloque inclus)
2021-02-02 - Article/Dans un journal avec peer-review - Anglais - 16 page(s)

Golzadeh Mehdi , Decan Alexandre , Legay Damien , Mens Tom , "A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments" in Journal of Systems and Software, 175, https://doi.org/10.1016/j.jss.2021.110911

  • Edition : Elsevier (Netherlands)
  • 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) Bots are frequently used in Github repositories to automate repetitive activities that are part of the distributed software development process. They communicate with human actors through comments. While detecting their presence is important for many reasons, no large and representative ground- truth dataset is available, nor are classification models to detect and validate bots on the basis of such a dataset. This paper proposes a ground-truth dataset, based on a manual analysis with high interrater agreement, of pull request and issue comments in 5,000 distinct Github accounts of which 527 have been identified as bots. Using this dataset we propose an automated classification model to detect bots, taking as main features the number of empty and non-empty comments of each account, the number of comment patterns, and the inequality between comments within comment patterns. We obtained a very high weighted average precision, recall and F1-score of 0.98 on a test set containing 40% of the data. We integrated the classification model into an open source command-line tool to allow practitioners to detect which accounts in a given Github repository actually correspond to bots.

Identifiants :
  • DOI : 10.1016/j.jss.2021.110911
  • FNRS : J.0151.20
  • FNRS : T.0017.18
  • FNRS : O.0157.18F-RG43

Mots-clés :
  • (Anglais) software repository mining
  • (Anglais) collaborative software development
  • (Anglais) software evolution