An Ant Colony Optimization Approach to the Software Release Planning with Dependent Requirements

  • Jerffeson Teixeira de Souza
  • Camila Loiola Brito Maia
  • Thiago do Nascimento Ferreira
  • Rafael Augusto Ferreira do Carmo
  • Márcia Maria Albuquerque Brasil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6956)

Abstract

Ant Colony Optimization (ACO) has been successfully employed to tackle a variety of hard combinatorial optimization problems, including the traveling salesman problem, vehicle routing, sequential ordering and timetabling. ACO, as a swarm intelligence framework, mimics the indirect communication strategy employed by real ants mediated by pheromone trails. Among the several algorithms following the ACO general framework, the Ant Colony System (ACS) has obtained convincing results in a range of problems. In Software Engineering, the effective application of ACO has been very narrow, being restricted to a few sparse problems. This paper expands this applicability, by adapting the ACS algorithm to solve the well-known Software Release Planning problem in the presence of dependent requirements. The evaluation of the proposed approach is performed over 72 synthetic datasets and considered, besides ACO, the Genetic Algorithm and Simulated Annealing. Results are consistent to show the ability of the proposed ACO algorithm to generate more accurate solutions to the Software Release Planning problem when compared to Genetic Algorithm and Simulated Annealing.

Keywords

Ant Colony Optimization Search Based Software Engineering Software Release Planning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jerffeson Teixeira de Souza
    • 1
  • Camila Loiola Brito Maia
    • 1
  • Thiago do Nascimento Ferreira
    • 1
  • Rafael Augusto Ferreira do Carmo
    • 1
  • Márcia Maria Albuquerque Brasil
    • 1
  1. 1.Optimization in Software Engineering Group (GOES.UECE)State University of Ceará (UECE)FortalezaBrazil

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