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Clustering technique for conceptual clusters

Brice Govin 1, 2 Arnaud Monegier Du Sorbier 1 Nicolas Anquetil 2 Stéphane Ducasse 2 
2 RMOD - Analyses and Languages Constructs for Object-Oriented Application Evolution
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : Clustering aims to classify elements into groups called classes or clusters. Clustering is used in reverse-engineering to help to understand legacy software. It is also a tech-nic used in re-engineering to propose gatherings of software entities to engineers who can then accept them or not. This paper presents a Pharo implementation of an iterative and semi-automatic method for clustering. Our method proposes, to an end-user, clusters that are based on domain information and structural information. The method presented in this paper has been applied in an industrial project of architecture migration. We show that this method helps engineers to cluster software elements into domain concepts. The clustering gives a result of 56% of precision and 79% of recall after the automated part in a high level clustering. A deeper clustering gives a result of 51% of precision and 52% of recall.
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Brice Govin, Arnaud Monegier Du Sorbier, Nicolas Anquetil, Stéphane Ducasse. Clustering technique for conceptual clusters. IWST'16 International Workshop on Smalltalk Technologies, Aug 2016, Prague, Czech Republic. ⟨10.1145/2991041.2991052⟩. ⟨hal-01353205⟩

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