Proceedings of the 8th international ACM SIGSOFT conference on Quality of Software Architectures
Improving software modularization via automated analysis of latent topics and dependencies
ACM Transactions on Software Engineering and Methodology (TOSEM)
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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.