Bi-criteria genetic search for adding new features into an existing product line

Abstract

Software product line evolution involves decisions like finding which products are better candidates for realizing new feature requests. In this paper, we propose a solution for finding trade-off evolution alternatives for products while balancing between overall value and product integrity. The purpose of this study is to support product managers with feature selection for an existing product line. For this purpose, first, the feature model of the product line is encoded into a single binary encoding. Then we employ a bi-criteria genetic search algorithm, NSGA-II, to find the possible alternatives with different value and product integrity. From the proposed set of trade-off alternatives, the product line manager can select the solutions that best fit with the concerns of their preference. The implementation has been initially evaluated by two product line configurations.