A genetic algorithm for optimized feature selection with resource constraints in software product lines

  • Authors:

  • Jianmei Guo;Jules White;Guangxin Wang;Jian Li;Yinglin Wang

  • Affiliations:

  • Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China;Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA 24060, USA;Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang, Shanghai 200240, China

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Abstract: Software product line (SPL) engineering is a software engineering approach to building configurable software systems. SPLs commonly use a feature model to capture and document the commonalities and variabilities of the underlying software system. A key challenge when using a feature model to derive a new SPL configuration is determining how to find an optimized feature selection that minimizes or maximizes an objective function, such as total cost, subject to resource constraints. To help address the challenges of optimizing feature selection in the face of resource constraints, this paper presents an approach that uses G enetic A lgorithms for optimized FE ature S election (GAFES) in SPLs. Our empirical results show that GAFES can produce solutions with 86-97% of the optimality of other automated feature selection algorithms and in 45-99% less time than existing exact and heuristic feature selection techniques.