arXiv 2408.091282024-08-17cs.SEcs.AI

跨多样软件项目Issue识别技术债务及其类型

Identifying Technical Debt and Its Types Across Diverse Software Projects Issues

University of Oslo（奥斯陆大学）；Visma Software International（Visma软件国际公司）

Karthik Shivashankar, Mili Orucevic, Maren Maritsdatter Kruke, Antonio Martini

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AI总结：

本研究针对软件项目Issue的技术债务识别需求，提出基于集成学习的多二分类器Transformer方案，经多数据集验证，项目内微调的小模型DistilRoBERTa表现更优，为高效精准的技术债务检测提供了可行路径。

摘要：

软件项目Issue中的技术债务（Technical Debt，TD）识别对于维持代码质量、降低长期维护成本以及改善项目整体健康度至关重要。本研究利用基于Transformer的模型推进TD分类，满足大规模软件开发中精准高效识别TD的关键需求。\n 我们的方法采用多个针对TD及其类型的二分类器，通过集成学习组合，以提升检测各类TD的准确率与鲁棒性。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。\n 我们证明，项目内微调的Transformer模型在TD分类中显著优于任务特定微调模型，凸显了项目特定上下文对精准TD识别的重要性。研究还发现，专用二分类器在TD及其类型识别上优于多分类模型，可支持更具针对性的债务解决策略。对比分析显示，较小的DistilRoBERTa模型在TD分类任务中比GPT等更大的语言模型更有效，尤其是在微调后，为特定TD检测任务的高效模型选择提供了见解。\n 本研究还使用MCC、AUC ROC、召回率（Recall）和F1值等指标评估泛化能力，重点关注模型效果、微调影响及相对性能。通过在分布外和真实工业数据集上验证我们的方法，我们确保了实际适用性，应对软件项目的多样性特征。

英文摘要：

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.

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