Technical Debt (TD) identification in software projects issues is crucial for maintaining code quality, reducing long-term maintenance costs, and improving overall project health. This study advances TD classification using transformer-based models, addressing the critical need for accurate and efficient TD identification in large-scale software development. Our methodology employs multiple binary classifiers for TD and its type, combined through ensemble learning, to enhance accuracy and robustness in detecting various forms of TD. We train and evaluate these models on a comprehensive dataset from GitHub Archive Issues (2015-2024), supplemented with industrial data validation. We demonstrate that in-project fine-tuned transformer models significantly outperform task-specific fine-tuned models in TD classification, highlighting the importance of project-specific context in accurate TD identification. Our research also reveals the superiority of specialized binary classifiers over multi-class models for TD and its type identification, enabling more targeted debt resolution strategies. A comparative analysis shows that the smaller DistilRoBERTa model is more effective than larger language models like GPTs for TD classification tasks, especially after fine-tuning, offering insights into efficient model selection for specific TD detection tasks. The study also assesses generalization capabilities using metrics such as MCC, AUC ROC, Recall, and F1 score, focusing on model effectiveness, fine-tuning impact, and relative performance. By validating our approach on out-of-distribution and real-world industrial datasets, we ensure practical applicability, addressing the diverse nature of software projects.

翻译：软件项目问题中的技术债务识别对于维护代码质量、降低长期维护成本以及提升项目整体健康度至关重要。本研究通过采用基于Transformer的模型推进技术债务分类，以应对大规模软件开发中对准确高效技术债务识别的迫切需求。我们的方法采用多个针对技术债务及其类型的二元分类器，并通过集成学习进行结合，以提升检测各类技术债务形式的准确性与鲁棒性。我们在来自GitHub Archive Issues（2015-2024）的综合数据集上训练并评估这些模型，并辅以工业数据验证。我们证明，在项目内微调的Transformer模型在技术债务分类任务上显著优于任务特定微调模型，这凸显了项目特定语境在准确识别技术债务中的重要性。我们的研究还揭示了，对于技术债务及其类型识别，专门的二元分类器优于多类别模型，从而能够实现更具针对性的债务解决策略。比较分析表明，较小的DistilRoBERTa模型在技术债务分类任务上比GPTs等大型语言模型更为有效，尤其是在微调之后，这为特定技术债务检测任务的高效模型选择提供了洞见。本研究还使用MCC、AUC ROC、召回率和F1分数等指标评估模型的泛化能力，重点关注模型有效性、微调影响及相对性能。通过在分布外数据和真实世界工业数据集上验证我们的方法，我们确保了其实际适用性，以应对软件项目的多样性。