Evaluating Large Language Models on the Classification of Different Technical Debt Types in Stack Overflow Discussions - Amaral, Lucas; Gama, Eliakim; Paixao, Matheus; Aguiar, Lucas (Anais do Workshop Brasileiro de Engenharia de Software Inteligente (ISE); 2025: Anais do IV Workshop Brasileiro de Engenharia de Software InteligenteDO - 10.5753/ise.2025.14868, 2025)
Technical Debt (TD) refers to suboptimal decisions made during software development that offer short-term benefits at the cost of long-term maintainability. Managing TD is critical for ensuring the sustainability of software systems, especially as projects evolve. While prior research has leveraged machine learning techniques to identify TD in data from platforms such as Stack Overflow (SO), those approaches have shown limited performance. To address these limitations, this study investigates the effectiveness of transformer-based Large Language Models (LLMs) for the automated identification and classification of TD types in SO discussions. We evaluated three prominent LLMs: BERT, BART, and GPT-2, on their ability to classify multiple types of TD. Our contributions are: (i) a reproducible training/evaluation pipeline on an SO TD dataset, and (ii) a comparison against prior studies. LLMs reach up to 85% F1 and 78.6% average F1, outperforming previous results by 8–23%.