Software Defect Prediction Based on Semantic Views of Metrics: Clustering Analysis and Model Performance Analysis

In recent years, with the rapid development of software systems, the continuous expansion of software scale and the increasing complexity of systems have led to the emergence of a growing number of software metrics. Defect prediction methods based on software metric elements highly rely on software metric data. However, redundant software metric data is not conducive to efficient defect prediction, posing severe challenges to current software defect prediction tasks. To address these issues, this paper focuses on the rational clustering of software metric data. Firstly, multiple software projects are evaluated to determine the preset number of clusters for software metrics, and various clustering methods are employed to cluster the metric elements. Subsequently, a co-occurrence matrix is designed to comprehensively quantify the number of times that metrics appear in the same category. Based on the comprehensive results, the software metric data are divided into two semantic views containing different metrics, thereby analyzing the semantic information behind the software metrics. On this basis, this paper also conducts an in-depth analysis of the impact of different semantic view of metrics on defect prediction results, as well as the performance of various classification models under these semantic views. Experiments show that the joint use of the two semantic views can significantly improve the performance of models in software defect prediction, providing a new understanding and approach at the semantic view level for defect prediction research based on software metrics.