RESEARCH NOTES: Multiclass Classification for Self-Admitted Technical Debt via Large Pre-Trained Language Model

Technical debt refers to suboptimal solutions adopted for short-term goals. Self-admitted technical debt (SATD) is the debt that is explicitly marked through comments or documentation, making it traceable. Multi-classification of SATD helps developers understand different debt types and improve efficiency. This paper proposes a SATD multi-classification method based on Fine-Tuning the GPT-3.5-turbo model for SATD prediction. This study uses a public dataset containing 10 projects with code comments. We classify design debt, requirement debt, and defect debt and evaluate our method’s performance. The experimental results show that compared to the best baseline model, our method achieves average improvements of 11.41%, 1.72% and 3.72% in MacroF, MacroP and MacroR metrics, respectively, in the MTO scenario. In the OTO scenario, improvements are 2.33%, 3.70% and 2.18%, respectively. These results indicate that our method has a strong generalization ability in SATD multi-classification and offers a new approach to managing technical debt.