用大模型增强数据，提升技术债识别准确率
Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt
- 用BiLSTM做二分类，BERT做类型分类
- 数据增强后，测试与需求债识别率提升显著
- 提供平衡数据集，适合软件质量研究者使用
自承认技术债（SATD）指开发者通过文本说明当前实现不理想的状况。现有方法多聚焦于识别（判断是否为SATD）或分类（区分需求、设计、代码、测试等类型），但性能不佳，尤其在测试与需求债类别上，主要因数据极度不平衡。为此，我们采用BiLSTM进行二分类，BERT进行类型分类，并引入大语言模型进行数据增强以缓解不平衡问题。提出两步法，在多种来源的文本数据上实现识别与分类。贡献包括构建一个平衡数据集供后续研究使用，且实验表明该方法显著优于基线模型。
原文摘要 · Abstract (English)
Self-Admitted Technical Debt (SATD) refers to circumstances where developers use textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD (classifying SATD items as SATD or not) or categorizing SATD (labeling instances as SATD that pertain to requirement, design, code, test debt, etc.). However, the performance of these approaches remains suboptimal, particularly for specific types of SATD, such as test and requirement debt, primarily due to extremely imbalanced datasets. To address these challenges, we build on earlier research by utilizing BiLSTM architecture for the binary identification of SATD and BERT architecture for categorizing different types of SATD. Despite their effectiveness, both architectures struggle with imbalanced data. Therefore, we employ a large language model data augmentation strategy to mitigate this issue. Furthermore, we introduce a two-step approach to identify and categorize SATD across various datasets derived from different artifacts. Our contributions include providing a balanced dataset for future SATD researchers and demonstrating that our approach significantly improves SATD identification and categorization performance compared to baseline methods.
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