Feature Model Synthesis with Genetic Programming

  • Lukas Linsbauer
  • Roberto Erick Lopez-Herrejon
  • Alexander Egyed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8636)

Abstract

Search-Based Software Engineering (SBSE) has proven successful on several stages of the software development life cycle. It has also been applied to different challenges in the context of Software Product Lines (SPLs) like generating minimal test suites. When reverse engineering SPLs from legacy software an important challenge is the reverse engineering of variability, often expressed in the form of Feature Models (FMs). The synthesis of FMs has been studied with techniques such as Genetic Algorithms. In this paper we explore the use of Genetic Programming for this task. We sketch our general workflow, the GP pipeline employed, and its evolutionary operators. We report our experience in synthesizing feature models from sets of feature combinations for 17 representative feature models, and analyze the results using standard information retrieval metrics.

Keywords

Feature Feature Models Feature Set Reverse Engineering Software Product Lines Variability Modeling 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lukas Linsbauer
    • 1
  • Roberto Erick Lopez-Herrejon
    • 1
  • Alexander Egyed
    • 1
  1. 1.Software Systems EngineeringJohannes Kepler UniversityLinzAustria

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