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Social coding platforms have revolutionized collaboration in softwaredevelopment, leading to using software bots for streamlining operations.However, The presence of open-source software (OSS) bots gives rise to problemsincluding impersonation, spamming, bias, and security risks. Identifying botaccounts and behavior is a challenging task in the OSS project. This researchaims to investigate bots' behavior in open-source software projects andidentify bot accounts with maximum possible accuracy. Our team gathered adataset of 19,779 accounts that meet standardized criteria to enable futureresearch on bots in open-source projects. We follow a rigorous workflow toensure that the data we collect is accurate, generalizable, scalable, andup-to-date. We've identified four types of bot accounts in open-source softwareprojects by analyzing their behavior across 17 features in 5 dimensions. Ourteam created BotHawk, a highly effective model for detecting bots inopen-source software projects. It outperforms other models, achieving an AUC of0.947 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 offollowers, number of repositories, and tags contain the most relevant featuresto identify the account type.

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