BotHawk: An Approach for Bots Detection in Open Source Software Projects
| Autor: | Bi, Fenglin, Zhu, Zhiwei, Wang, Wei, Xia, Xiaoya, Khan, Hassan Ali, Pu, Peng |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Dataset, Bots Detection, Classification. Open-source Software Bots |
| Databáze: | arXiv |
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