Class Imbalance-oriented Online Feature Selection Method for Just-in-time Software Defect Prediction

Just-in-time software defect prediction (JIT-SDP) is a defect prediction technique that targets changes in software code, offering significant advantages in quickly identifying potential defects and improving development efficiency. However, most existing methods assume that the importance of features remains stable over time, overlooking the dynamic changes in feature distributions and the evolution of class imbalance in real-world development environments. This limitation eventually degrades the predictive performance. To address this issue, this paper proposes an Imbalance-oriented Online Feature Selection (IOFS) method, which dynamically adjusts the feature importance and uncertainty parameters to adapt in real time to concept drift and class imbalance in data streams, thereby enhancing model performance and generalization. The experimental validation on 14 open-source project datasets demonstrates that IOFS significantly improves the values of [Formula: see text]Mean on 11 datasets and effectively reduces the average of the absolute differences between recalls for each time step, exhibiting robustness to dynamic feature changes and sensitivity to development-phase feature differences. This study provides an effective solution for online JIT-SDP.