Approximate Backbone Based Multilevel Algorithm for Next Release Problem

TL;DR

This paper introduces an approximate backbone based multilevel algorithm (ABMA) for the Next Release Problem, effectively solving large-scale instances by leveraging backbone approximation and multilevel search techniques.

Contribution

It presents a novel multilevel algorithm that uses backbone approximation to efficiently address large-scale NP-hard requirements selection problems.

Findings

ABMA outperforms existing algorithms in solution quality.

ABMA reduces running time significantly.

Backbone approximation effectively guides large-scale problem solving.

Abstract

The next release problem (NRP) aims to effectively select software requirements in order to acquire maximum customer profits. As an NP-hard problem in software requirement engineering, NRP lacks efficient approximate algorithms for large scale instances. The backbone is a new tool for tackling large scale NP-hard problems in recent years. In this paper, we employ the backbone to design high performance approximate algorithms for large scale NRP instances. Firstly we show that it is NP-hard to obtain the backbone of NRP. Then, we illustrate by fitness landscape analysis that the backbone can be well approximated by the shared common parts of local optimal solutions. Therefore, we propose an approximate backbone based multilevel algorithm (ABMA) to solve large scale NRP instances. This algorithm iteratively explores the search spaces by multilevel reductions and refinements. Experimental results demonstrate that ABMA outperforms existing algorithms on large instances in terms of solution quality and running time.