In the context of Just-In-Time Software Defect Prediction (JIT-SDP), Concept drift (CD) can occur due to changes in the software development process, the complexity of the software, or changes in user behavior that may affect the stability of the JIT-SDP model over time. Additionally, the challenge of class imbalance in JIT-SDP data poses a potential risk to the accuracy of CD detection methods if rebalancing is implemented. This issue has not been explored to the best of our knowledge. Furthermore, methods to check the stability of JIT-SDP models over time by considering labeled evaluation data have been proposed. However, it should be noted that future data labels may not always be available promptly. We aim to develop a reliable JIT-SDP model using CD point detection directly by identifying changes in the interpretation of unlabeled simplified and resampled data. To evaluate our approach, we first obtained baseline methods based on model performance monitoring to identify CD points on labeled data. We then compared the output of the proposed methods with baseline methods based on performance monitoring of threshold-dependent and threshold-independent criteria using well-known performance measures in CD detection methods, such as accuracy, MDR, MTD, MTFA, and MTR. We also utilize the Friedman statistical test to assess the effectiveness of our approach. As a result, our proposed methods show higher compatibility with baseline methods based on threshold-independent criteria when applied to rebalanced data, and with baseline methods based on threshold-dependent criteria when applied to simple data.

本文提出了一种可靠的 JIT-SDP 模型，通过识别未标记的简化和重采样数据的解释的变化，直接使用 CD 点检测。使用阈值独立和阈值依赖的基准方法进行比较，并使用 Friedman 统计检验评估方法的有效性。结果表明，当应用于重新平衡数据时，我们提出的方法与基于阈值独立标准的基准方法更兼容；当应用于简单数据时，我们提出的方法与基于阈值依赖标准的基准方法更兼容。

使用实例解释检测软件缺陷可靠性预测中的概念漂移