In this paper we present a clustering based approach to partition software systems into meaningful subsystems. In particular, the approach uses lexical information extracted from four zones in Java classes, which may provide a different contribution towards software systems partitioning. To automatically weigh these zones, we introduced a probabilistic model, and applied the Expectation-Maximization (EM) algorithm. To group classes according to the considered lexical information, we customized the well-known K-Medoids algorithm. To assess the approach and the implemented supporting system, we have conducted a case study on six open source software systems.
A Probabilistic based Approach towards Software System Clustering / Corazza, Anna; DI MARTINO, Sergio; G., Scanniello. - STAMPA. - (2010), pp. 89-97. (Intervento presentato al convegno 14th European Conference on Software Maintenance and Reengineering (CSMR 2010)).
A Probabilistic based Approach towards Software System Clustering
CORAZZA, ANNA;DI MARTINO, SERGIO;
2010
Abstract
In this paper we present a clustering based approach to partition software systems into meaningful subsystems. In particular, the approach uses lexical information extracted from four zones in Java classes, which may provide a different contribution towards software systems partitioning. To automatically weigh these zones, we introduced a probabilistic model, and applied the Expectation-Maximization (EM) algorithm. To group classes according to the considered lexical information, we customized the well-known K-Medoids algorithm. To assess the approach and the implemented supporting system, we have conducted a case study on six open source software systems.File | Dimensione | Formato | |
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