Methodology for Determining the Optimal Clustering Algorithm for Software Quality Verification
Keywords:
software quality, software metrics, clustering, K-Means, DBSCAN, OPTICS, Affinity Propagation, Gaussian Mixture, DSSAbstract
The article examines methodologies for evaluating the quality of clustering algorithms used to identify patterns within codebases in the context of a decision support system (DSS) module for software quality verification in information and communication systems. A novel feature dictionary is introduced, wherein evaluation metrics represent a software class as an implementation vector. These metrics are preselected based on the most salient characteristics of programming code. The five widely recognized clustering algorithms - namely, K-Means, DBSCAN, OPTICS, Affinity Propagation, and Gaussian Mixture Models are evaluated in this study. The proposed methodology is applied to five Java application projects that implement diverse architectural solutions and software patterns. These applications are distributed under an open license and are readily accessible for research purposes. The source code of the selected software is transformed into vectors by extracting relevant code characteristics, thereby facilitating subsequent training. The results obtained confirmed the suitability of the proposed feature vector, and the optimal clustering model was subsequently selected for integration into the decision-making system module for quality assessment in information and communication systems.
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