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Remodularization Analysis Using Semantic Clustering

Gustavo Santos 1 Marco Tulio Valente 1 Nicolas Anquetil 2
1 Department of Computer Science [Minas Gerais]
UFMG - Universidade Federal de Minas Gerais [Belo Horizonte]
2 RMOD - Analyses and Languages Constructs for Object-Oriented Application Evolution
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : In this paper, we report an experience on using and adapting Semantic Clustering to evaluate software remodularizations. Semantic Clustering is an approach that relies on information retrieval and clustering techniques to extract sets of similar classes in a system, according to their vocabularies. We adapted Semantic Clustering to support remodularization analysis. We evaluate our adaptation using six real-world remodularizations of four software systems. We report that Semantic Clustering and conceptual metrics can be used to express and explain the intention of the architects when performing common modularization operators, such as module decomposition.
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Gustavo Santos, Marco Tulio Valente, Nicolas Anquetil. Remodularization Analysis Using Semantic Clustering. 1st CSMR-WCRE Software Evolution Week, Feb 2014, Antwerp, Belgium. ⟨10.1109/CSMR-WCRE.2014.6747174⟩. ⟨hal-00904409⟩

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