Tarimliq: A new internal metric for software clustering analysis

Abstract

Clustering techniques are utilized in the maintenance process of a software system to understand it. Different clustering algorithms have been proposed for this purpose in the literature. In the field of software clustering, a number of external criteria are presented to evaluate and validate the obtained clusters. External criteria use a reference clustering to evaluate an achieved clustering. Because of the comparison with reference clustering, the validity and accuracy of these methods are reliable in the assessment. When there is no reference clustering, internal criteria are used to validate clustering algorithms. Since there is no internal criterion for evaluating software clustering algorithms, the internal criteria available in data clustering are employed. In this paper, we propose an internal metric for evaluating software clustering algorithms. The results on Mozilla Firefox, as a large-scale software, demonstrate that the proposed internal metric is more accurate than the tested internal criteria and can also be a suitable alternative for external criteria.