Print Email Facebook Twitter BotHunter Title BotHunter: An Approach to Detect Software Bots in GitHub Author Abdellatif, Ahmad (Concordia University) Wessel, Mairieli (TU Delft Software Engineering) Steinmacher, Igor (Universidade Tecnológica Federal Do Paraná (UTFPR)) Gerosa, Marco A. (Northern Arizona University) Shihab, Emad (Concordia University) Date 2022 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. Subject Empirical Software EngineeringSoftware Bots To reference this document use: http://resolver.tudelft.nl/uuid:a1c4fc01-5e10-46d9-b531-b718070ed63e DOI https://doi.org/10.1145/3524842.3527959 Publisher Institute of Electrical and Electronics Engineers (IEEE) Embargo date 2023-06-01 ISBN 9781450393034 Source Proceedings - 2022 Mining Software Repositories Conference, MSR 2022 Event 2022 Mining Software Repositories Conference, MSR 2022, 2022-05-23 → 2022-05-24, Pittsburgh, United States Series Proceedings - 2022 Mining Software Repositories Conference, MSR 2022 Part of collection Institutional Repository Document type conference paper Rights © 2022 Ahmad Abdellatif, Mairieli Wessel, Igor Steinmacher, Marco A. Gerosa, Emad Shihab Files PDF 3524842.3527959.pdf 768.71 KB Close viewer /islandora/object/uuid:a1c4fc01-5e10-46d9-b531-b718070ed63e/datastream/OBJ/view