Clustering technique for conceptual clusters

Govin, Brice; Monegier Du Sorbier, Arnaud; Anquetil, Nicolas; Ducasse, Stephane

Type de document :

Communication dans un congrès avec actes

DOI :

10.1145/2991041.2991052

Titre :

Clustering technique for conceptual clusters

Auteur(s) :

Govin, Brice [Auteur]
Thales Air Systems
Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Monegier Du Sorbier, Arnaud [Auteur]
Thales Air Systems
Anquetil, Nicolas [Auteur]

Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]
Ducasse, Stephane [Auteur]

Analyses and Languages Constructs for Object-Oriented Application Evolution [RMOD]

Titre de la manifestation scientifique :

IWST'16 International Workshop on Smalltalk Technologies

Ville :

Prague

Pays :

République tchèque

Date de début de la manifestation scientifique :

2016-08-22

Titre de la revue :

Proceedings of the 11th edition of the International Workshop on Smalltalk Technologies

Discipline(s) HAL :

Informatique [cs]/Langage de programmation [cs.PL]
Informatique [cs]/Génie logiciel [cs.SE]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL

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Clustering technique for conceptual clusters

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