Dynamic Feature Selection Based on Model Interpretation for Just-in-Time Software Defect Prediction

Just-In-Time Software Defect Prediction (JIT-SDP) aims to predict defects for each code change submitted by developers. Compared with traditional techniques, it offers advantages such as fine granularity, immediacy and traceability. However, most existing JIT-SDP models primarily focus on improving prediction performance, while research on model interpretability remains limited, especially with little attention given to time-series factor. Therefore, this study investigates the interpretability of JIT-SDP models in time-series scenario. It employs model interpretation techniques to analyze feature importance, explores the effectiveness of interpretability-based feature selection methods and proposes a SHAP-based Dynamic Feature Selection (SDFS) method that leverages model explanations in accordance with the temporal evolution of JIT-SDP. The experimental results demonstrate that certain features consistently show high importance across datasets, interpretability-based feature selection methods can enhance prediction performance and the proposed SDFS method effectively improves the performance of defect prediction models.