Papers › Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt

Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt

21 Oct 2024arXiv:2410.15804archive 2025-07-28

Edi Sutoyo, Paris Avgeriou, Andrea Capiluppi

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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Data Augmentation Language Modeling Language Modelling Large Language Model

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Adam Attention Attention Dropout BERT BiLSTM Dense Connections Dropout LSTM Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Sigmoid Activation Softmax Tanh Activation Weight Decay WordPiece

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