Reducing the Class Coupling of Legacy Code by a Metrics-Based Relocation of Class Members - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

Reducing the Class Coupling of Legacy Code by a Metrics-Based Relocation of Class Members

(1) , (1) , (2) , (1) , (2) , (2)
1
2
Marvin Ferber
  • Function : Author
  • PersonId : 1008832
Sascha Hunold
  • Function : Author
  • PersonId : 1008833
Thomas Rauber
  • Function : Author
  • PersonId : 1008835

Abstract

With the rapid growth of the complexity of software systems, the problem of integrating and maintaining legacy software is more relevant than ever. To overcome this problem, many methods for refactoring legacy code have already been proposed such as renaming classes or extracting interfaces. To perform a real modularization, methods have to be moved between classes. However, moving a single method is often not possible due to code dependencies.In this article we present an approach to modularize legacy software by moving multiple related class members. It is shown how to identify groups of class members with similar concerns. We present two different code patterns that the related members and their dependent classes must match to allow a relocation of the related members. We also demonstrate how our pattern-based approach for automated modularization of legacy software can be applied to two open source projects.
Fichier principal
Vignette du fichier
978-3-642-28038-2_16_Chapter.pdf (2.98 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01527393 , version 1 (24-05-2017)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Marvin Ferber, Sascha Hunold, Björn Krellner, Thomas Rauber, Thomas Reichel, et al.. Reducing the Class Coupling of Legacy Code by a Metrics-Based Relocation of Class Members. 4th Central and East European Conference on Software Engineering Techniques (CEESET), Oct 2009, Krakow, Poland. pp.202-214, ⟨10.1007/978-3-642-28038-2_16⟩. ⟨hal-01527393⟩
86 View
64 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More