BotHawk: An Approach for Bots Detection in Open Source Software Projects

• URL: http://arxiv.org/abs/2307.13386v1
• Date: Tue, 25 Jul 2023 10:15:38 GMT
• Title: BotHawk: An Approach for Bots Detection in Open Source Software Projects
• Authors: Fenglin Bi, Zhiwei Zhu, Wei Wang, Xiaoya Xia, Hassan Ali Khan, Peng Pu
• Abstract summary: This research aims to investigate bots' behavior in open-source software projects and identify bot accounts with maximum possible accuracy. We've identified four types of bot accounts in open-source software projects by analyzing their behavior across 17 features in 5 dimensions. Our team created BotHawk, a highly effective model for detecting bots in open-source software projects.
• Score: 4.59229477803039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social coding platforms have revolutionized collaboration in software development, leading to using software bots for streamlining operations. However, The presence of open-source software (OSS) bots gives rise to problems including impersonation, spamming, bias, and security risks. Identifying bot accounts and behavior is a challenging task in the OSS project. This research aims to investigate bots' behavior in open-source software projects and identify bot accounts with maximum possible accuracy. Our team gathered a dataset of 19,779 accounts that meet standardized criteria to enable future research on bots in open-source projects. We follow a rigorous workflow to ensure that the data we collect is accurate, generalizable, scalable, and up-to-date. We've identified four types of bot accounts in open-source software projects by analyzing their behavior across 17 features in 5 dimensions. Our team created BotHawk, a highly effective model for detecting bots in open-source software projects. It outperforms other models, achieving an AUC of 0.947 and an F1-score of 0.89. BotHawk can detect a wider variety of bots, including CI/CD and scanning bots. Furthermore, we find that the number of followers, number of repositories, and tags contain the most relevant features to identify the account type.

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