Experimenting with Evolutionary Algorithms to Reduce Feature Model Configuration Steps

EasyChair Preprint 5974

Abstract

In the software engineering world, software product lines constitute an approach to building reliable software systems. These use feature models to capture, develop, and document shared software for a base system. One of the main challenges when using feature models to derive new products configuration is a way of selecting a configuration that takes under consideration the minimum number of steps and minimum decision-making cost, taking into account resource constraints. To satisfy the challenges of optimizing the configuration selection technique, in this paper, we present an assessment approach that makes use of genetic algorithms to generate the best product configurations from feature models. Our empirical outcomes reveal the effectiveness of the proposed approach in obtaining product configurations that meet the feature model constraints with minimum steps and decision cost, consequently, assist customers in selecting the product configuration that fits their requirements.

Keyphrases: Feature Model, Genetic Algorithm, Software Engineering, Software Product Line, product configuration

@booklet{EasyChair:5974,
  author    = {Dalia Owdeh and Abdel Salam Sayad},
  title     = {Experimenting with Evolutionary Algorithms to Reduce Feature Model Configuration Steps},
  howpublished = {EasyChair Preprint 5974},
  year      = {EasyChair, 2021}}