Abstract: In open-source software,accurately identifying  software development assistant robots(Bots) and human contributors is crucial for understanding and evaluating contribution activities.Given the outstanding performance of deep learning models in NLP and software engineering-related fields,this paper proposes BotChecker,a Transformer-based automated bot detection model.By incorporating enhanced fully connected layers and a dedicated binary classifier structure into the Transformer,the model can effectively learn from comment text data of bot and human accounts to detect bots.Experiments validate the effectiveness of BotChecker in bot detection tasks,achieving Accuracy,Recall,and F1-score of 0.941,0.894,and 0.938,respectively.Furthermore,this paper analyzes the impact of model hyperparameters and zero-shot learning on BotChecker’s performance.The proposed model can provide technical support for bot account identification in open-source communities and serve as a methodological benchmark for future research.

Key words: open-source platform, Bot detection technique, empirical analysis, text processing