Crashing Fault Residence Prediction Using a Hybrid Feature Selection Framework from Multi-Source Data

The inherent complexity of modern software frequently leads to critical issues such as defects, performance degradation, and system failures. Among these, system crashes pose a severe threat to reliability, as they demand rapid fault localization to minimize downtime and restore functionality. A critical step of fault localization is predicting the residence of crashing faults, which involves determining whether a fault is located within the stack trace or outside it. This task plays a crucial role in software quality assurance by enhancing debugging efficiency and reducing testing costs. This study introduces SCM, a two-stage composite feature selection framework designed to address this challenge. The SCM framework integrates spectral clustering for feature grouping, which organizes highly correlated features into clusters while reducing redundancy and capturing non-linear relationships. Maximal information coefficient analysis is then applied to rank features within each cluster and select the most relevant ones, forming an optimized feature subset. A decision tree classifier is then applied to predict the residence of crashing faults. Extensive experiments on seven open-source software projects show that the SCM framework outperforms seven baseline methods, which include four classifiers and three ranking approaches, across four evaluation metrics such as F-measure, g-mean, MCC, and AUC. These results highlight its potential in improving fault localization.