An Empirical Study of Gradient-based Explainability Techniques for Self-admitted Technical Debt Detection,ERICDATA高等教育知識庫
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篇名
An Empirical Study of Gradient-based Explainability Techniques for Self-admitted Technical Debt Detection
並列篇名
An Empirical Study of Gradient-based Explainability Techniques for Self-admitted Technical Debt Detection
作者 Guoqiang ZhuangYubin QuLong LiXianzhen DouMengao Li
英文摘要

Self-Admitted Technical Debt (SATD) is an intentionally introduced software code comment describing potential defects or other technical debt. Currently, deep learning is widely used in fields such as Natural Language Processing. Deep learning can be used for SATD detection, but there is a class imbalance problem and a large number of easily classified SATD instances that may potentially affect the loss value. As a result, we proposed a weighted focal loss function based on particle swarm to address the problem. Meanwhile, there is no empirical research based on local explanations for SATD detection. We have investigated local interpretation models such as Saliency Maps, Integrated Gradients, which are currently widely used in deep learning, and conducted empirical research for shared data sets. The research results show that our proposed weighted focal loss function can achieve the best performance for SATD detection; our model achieves 12.27%, 5.97%, and 5.62% improvement in Precision, Recall, and AUC compared to the baseline model, respectively; Local explanation models, including Saliency Maps and Integrated Gradients can cover nearly half of the manually labeled paradigms; these two interpretation models can also discover potential new paradigms.

 

起訖頁 631-641
關鍵詞 Self-Admitted Technical DebtDeep learningExplainabilityClass imbalanceFocal loss
刊名 網際網路技術學刊  
期數 202205 (23:3期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022052303021   複製DOI
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