  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.
