In collaborative software development, bots have become increasingly prevalent, making effective bot recommendation a key factor in enhancing development efficiency. This study aims to explore the application and efficacy of deep learning models in bot recommendation. Focusing on the CodeBERT model, we conduct a comprehensive evaluation through comparison with baseline models, parameter tuning (including batch size and learning rate), and the incorporation of language data. Our findings demonstrate that under specific conditions, the CodeBERT model exhibits superior performance in bot recommendation tasks, with parameter adjustments and the inclusion of language data significantly impacting the model's effectiveness. These insights offer new perspectives and strategies for the effective recommendation of bots in open-source software platforms.

Bots are prevalent contributors in collaborative software development, necessitating accurate detection techniques. This preliminary study aims to leveraging public datasets and Transformer-based models (i.e., BERT, CodeBERT, RoBERTa, BART, and PLBART) for the bot detection task. Our experimental result reveals that CodeBERT achieves the highest performance, with an impressive accuracy score of 94.1%.