Mining the impact of evolution categories on object-oriented metrics

  • Authors:

  • Henrique Rocha;Cesar Couto;Cristiano Maffort;Rogel Garcia;Clarisse Simoes;Leonardo Passos;Marco Tulio Valente

  • Affiliations:

  • Department of Computer Science, UFMG, Belo Horizonte, Brazil;Department of Computer Science, UFMG, Belo Horizonte, Brazil and Department of Computing, CEFET-MG, Minas Gerais, Brazil;Department of Computer Science, UFMG, Belo Horizonte, Brazil and Department of Computing, CEFET-MG, Minas Gerais, Brazil;Department of Computer Science, UFMG, Belo Horizonte, Brazil;Department of Computer Science, UFMG, Belo Horizonte, Brazil;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada;Department of Computer Science, UFMG, Belo Horizonte, Brazil

  • Venue:
  • Software Quality Control
  • Year:
  • 2013

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Abstract

Despite the relevance of the software evolution phase, there are few characterization studies on recurrent evolution growth patterns and on their impact on software properties, such as coupling and cohesion. In this paper, we report a study designed to investigate whether the software evolution categories proposed by Lanza can be used to explain not only the growth of a system in terms of lines of code (LOC), but also in terms of metrics from the Chidamber and Kemerer (CK) object-oriented metrics suite. Our results show that high levels of recall (ranging on average from 52 to 72 %) are achieved when using LOC to predict the evolution of coupling and size. For cohesion, we have achieved smaller recall rates (