## 2 Related Work  
For the task of predicting the residence of crashing faults, [PERSON] et al. [3] were the first to investigate this topic. Based on their denoted benchmark dataset, [PERSON] et al. [7] proposed employing an advanced transfer learning method called balanced distribution adaptation to conduct the crashing fault residence prediction task under the cross-project scenario. Moreover, they extended this work by proposing a novel feature selection and embedding framework, which added an information-gain-based selection operation and used a weighting strategy to address the class imbalanced characteristic of the initially collected crash data. [PERSON] et al. [8] applied imbalanced metric learning to the crash data, aiming to learn a new feature representation with higher quality. This method aims to simultaneously reduce the distance between crash instances which have consistent labels and increase the distance between crash instances which have distinct labels. In 2021, [PERSON] et al. [9] conducted a comprehensive comparative analysis to investigate the impact of more than 20 feature selection strategies from four families with more than 20 classifiers on the model performance of predicting the residence of crashing faults. The studies' feature selection strategies are typical filter-based or wrapper-based ones. Large-scale experiments showed that there exist significant differences in the performance of these feature selection strategies across district classifiers and software projects.  
For the applications of feature selection methods, researchers have employed such methods on many tasks in the field of SE. For the defect prediction task, which predicts defective software modules, [PERSON] et al. [10] pointed out that feature selection is an important process in the general framework of software defect prediction. [PERSON] et al. [11] proposed a self-designed feature selection framework to identify irrelevant and redundant features. [PERSON] et al. [12] proposed a BCF-WASPAS approach, integrating Einstein operators with bipolar complex fuzzy sets, enhancing feature selection for software defect prediction by jointly addressing positive and negative selection criteria. [PERSON] et al. [13] developed a multi-filter wrapper feature selection approach to further improve defect prediction performance. [PERSON] et al. [14] assessed the impact of over 30 feature selection strategies with random forest, and [PERSON] et al. [15] further expanded this analysis using 21 classifiers, confirming the value of feature selection for defect prediction. For the software effort estimation task, which estimates the amount of consumed resources in the software development process, [PERSON] et al. [16] conducted an empirical study on six bio-inspired and four non-bio-inspired selection methods, with genetic algorithms and harmony search showing the best performance. [PERSON] et al. [17] presented a neighborhood mutual information-based method and achieved a 24% average improvement on five out of six datasets when compared with three representative feature selection methods. [PERSON] et al. [18] introduced a neighborhood component analysis-based approach with multi-layer perceptrons to enhance accuracy on the Desharnais dataset. [PERSON] et al. [19] observed that three filter-based methods outperformed random selection in homogeneous ensemble models. In addition, feature selection methods are also applied to other SE tasks, such as the software product line [20; 21], code smell detection [22; 23], and fault localization [24; 25; 26].  
Our work draws inspiration from prior studies. However, our approach distinguishes itself by introducing a two-stage composite framework (SCM) that integrates feature clustering and relevance ranking to optimize feature representation. By grouping highly correlated features, this unified framework reduces redundancy while simultaneously enhancing feature relevance, effectively addressing the challenges of crashing fault residence prediction [27].