[PERSON]  
1 State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 400023, China;
When the fault matches one of the frames in the stack trace, debugging is significantly streamlined, allowing developers to focus their inspection on a manageable number of lines of code. This scenario is referred to as the fault being inside the stack trace. However, when the root cause resides outside the stack trace, developers must explore broader sections of the program, encompassing functions related to the entire call sequence [3]. Such cases demand considerably more resources, including time and effort, to identify and rectify the fault. This distinction between faults located inside or outside the stack trace has increasingly attracted the attention of researchers and practitioners. As a result, accurately predicting the residence of crashing faults has emerged as a critical software quality assurance activity within the domain of Software Engineering (SE). [PERSON] et al. [3] made the first attempt to study the issue of predicting the crashing fault residence and later released their benchmark dataset. They simulated some crash instances and collected a total of 89 features to characterize them. Actually, predicting the residence of the crashing faults is a binary classification task, which uses a machine learning (ML) model to determine whether a crashing fault instance is within the stack trace or not. Just like general ML applications, the performance of the used model is affected by the feature quality of the dataset. When the data contain many features, irrelevant and redundant features may negatively affect model performance. Thus, in order to obtain satisfactory performance for this task, the crash data need to be preprocessed for obtaining high-quality feature representation. In this paper, we focus on the challenge of predicting fault residence, particularly addressing the need for reducing redundant and irrelevant features in crash data to enhance classification performance. To tackle this challenge, we propose a novel two-stage composite feature selection framework to reserve the representative features. More specifically, the hybrid framework integrates feature clustering and relevance ranking to select an optimal feature subset. This hybrid framework includes two stages: first, the Spectral Clustering (SC) method [4; 5] is used to cluster the original features into multiple groups; then, the Maximal Information Coefficient (MIC) [6] is employed to rank the features using their MIC values in each cluster. The feature with the highest MIC value in each cluster is selected as the candidate member of the final feature subset. This selected feature subset has low redundancy among its features and a strong relationship with the crash labels. Since the hybrid feature selection framework combines the SC method for feature clustering and the MIC method for feature relevance analysis, we refer to the proposed method as SCM. After the two stages, we use a decision tree classifier to build the classification model based on the selected feature subset for residence prediction of the crashing faults. To measure the prediction performance of our proposed SCM framework for the residence of crashing faults, we used a dataset that contains seven open-source software projects denoted by [PERSON] et al. [3] and employed four indicators for performance evaluation. The experimental results elaborate that our proposed SCM framework achieves an average F-measure of 0.643, average g-mean of 0.759, MCC of 0.569, and average AUC of 0.780. Our proposed SCM framework consistently outperforms eight comparative methods across all projects and all four evaluation metrics. This work makes two main contributions. First, we develop a novel two-stage composite framework, SCM, which selects the most representative features from the crash data to predict the residence of crashing faults.