Deep Learning-Based Self-Admitted Technical Debt Detection Empirical Research

Yubin Qu Yubin Qu, Tie Bao Yubin Qu, Meng Yuan Tie Bao, Long Li Meng Yuan
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Abstract

Self-Admitted Technical Debt (SATD) is a workaround for current gains and subsequent software quality in software comments. Some studies have been conducted using NLP-based techniques or CNN-based classifiers. However, there exists a class imbalance problem in different software projects since the software code comments with SATD features are significantly less than those without Non-SATD. Therefore, to design a classification model with the ability of dealing with this class imbalance problem is necessary for SATD detection. We propose an improved loss function based on information entropy. Our proposed function is studied in a variety of application scenarios. Empirical research on 10 JAVA software projects is conducted to show the competitiveness of our new approach. We find our proposed approach can perform significantly better than state-of-the-art baselines.  
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基于深度学习的自承认技术债务检测实证研究
自我承认的技术债务(SATD)是软件评论中当前收益和后续软件质量的变通方法。一些研究使用基于nlp的技术或基于cnn的分类器进行。但是,由于具有SATD特征的软件代码注释明显少于不具有非SATD特征的软件代码注释,因此在不同的软件项目中存在类不平衡问题。因此,设计一个能够处理这种类不平衡问题的分类模型是SATD检测的必要条件。提出了一种改进的基于信息熵的损失函数。我们提出的功能在各种应用场景中进行了研究。对10个JAVA软件项目进行了实证研究,以显示我们的新方法的竞争力。我们发现我们提出的方法可以比最先进的基线执行得更好。
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