Multi-label commit message classification through p-tuning

Version control systems (VCS) play a crucial role by enabling developers to record changes, revert to previous versions, and coordinate work across distributed teams. In version control systems (e.g., GitHub), commit message serves as concise descriptions of code changes made during development. In our study, we evaluate the performance of multi-label commit message classification using p-tuning (learnable prompt templates) through three pre-trained models such as BERT, RoBERTa and DistilBERT. The experimental results demonstrate that RoBERTa model outperforms other two models in terms of the widely used evaluation metrics (e.g., achieving 81.99% F1 score).