BotHunter: An Approach to Detect Software Bots in GitHub

Ahmad Abdellatif, Mairieli Wessel, Igor Steinmacher, Marco A. Gerosa, Emad Shihab

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

3 Citations (Scopus)
29 Downloads (Pure)

Abstract

Bots have become popular in software projects as they play critical roles, from running tests to fixing bugs/vulnerabilities. However, the large number of software bots adds extra effort to practitioners and researchers to distinguish human accounts from bot accounts to avoid bias in data-driven studies. Researchers developed several approaches to identify bots at specific activity levels (issue/pull request or commit), considering a single repository and disregarding features that showed to be effective in other domains. To address this gap, we propose using a machine learning-based approach to identify the bot accounts regardless of their activity level. We selected and extracted 19 features related to the account's profile information, activities, and comment similarity. Then, we evaluated the performance of five machine learning classifiers using a dataset that has more than 5,000 GitHub accounts. Our results show that the Random Forest classifier performs the best, with an F1-score of 92.4% and AUC of 98.7%. Furthermore, the account profile information (e.g., account login) contains the most relevant features to identify the account type. Finally, we compare the performance of our Random Forest classifier to the state-of-the-art approaches, and our results show that our model outperforms the state-of-the-art techniques in identifying the account type regardless of their activity level.

Original languageEnglish
Title of host publicationProceedings - 2022 Mining Software Repositories Conference, MSR 2022
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6-17
Number of pages12
ISBN (Electronic)9781450393034
DOIs
Publication statusPublished - 2022
Event2022 Mining Software Repositories Conference, MSR 2022 - Pittsburgh, United States
Duration: 23 May 202224 May 2022
Conference number: 19

Publication series

NameProceedings - 2022 Mining Software Repositories Conference, MSR 2022

Conference

Conference2022 Mining Software Repositories Conference, MSR 2022
Abbreviated titleMSR 2022
Country/TerritoryUnited States
CityPittsburgh
Period23/05/2224/05/22

Keywords

  • Empirical Software Engineering
  • Software Bots

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