Abstract

Software defect prediction (SDP) aims to identify defect-prone modules before testing, but severe class imbalance and redundant software metrics often limit prediction performance. Many conventional feature selection methods estimate feature relevance with the original imbalanced empirical distribution and mainly emphasize marginal relevance or global classifier-oriented criteria, which may under-prioritize features that are informative for the minority defective class. To address this issue, this paper proposes SCAR-CMB, a simplified class-reweighted and interaction-aware feature selection method for imbalanced SDP. SCAR-CMB estimates feature-label dependency with a class-balanced empirical distribution, controls redundancy using weighted conditional dependency information, and incorporates an interaction-aware conditional-gain term as an auxiliary re-prioritization signal within a relevance-screened feature pool. Rather than performing full causal structure discovery or formal synergy estimation, SCAR-CMB adopts a Markov-blanket-inspired conditional dependency design as a practical guide for feature selection. The final configuration excludes both hardness-aware weighting and false discovery rate filtering. SCAR-CMB is evaluated on ten public NASA and PROMISE defect datasets under a leakage-free cross-validation protocol. Compared with seven representative baselines, SCAR-CMB achieves competitive overall performance and obtains the highest average defective-class recall, G-mean, and balanced accuracy. However, it is not uniformly superior across all metrics, and the recall advantage is not confirmed by the omnibus Friedman test. Additional mechanism-level, stability, and sensitivity analyses show that class reweighting changes feature prioritization, the selected feature subsets are relatively stable across folds, and the interaction-aware term provides limited and dataset-dependent auxiliary effects. Sensitivity analyses further indicate that the main conclusions are not solely determined by a specific feature budget, discretization-bin setting, or downstream classifier. Overall, SCAR-CMB should be interpreted as a practical minority-class-oriented feature selection method that provides a trade-off among defective-class detection, feature subset control, and computational cost.

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