# Solving the Large Scale Next Release Problem with a Backbone Based Multilevel Algorithm

## TL;DR

This paper introduces a Backbone based Multilevel Algorithm (BMA) to efficiently solve large scale Next Release Problems in software engineering, improving solution quality over direct methods by using multilevel reductions and refinements.

## Contribution

The paper presents a novel multilevel algorithm utilizing approximate and soft backbones to address large scale NRP, a significant advancement over existing direct solving approaches.

## Key findings

- BMA outperforms direct solving methods on large instances.
- The approach effectively extracts instances from open bug repositories.
- Experimental results show improved solution quality on multiple benchmarks.

## Abstract

The Next Release Problem (NRP) aims to optimize customer profits and requirements selection for the software releases. The research on the NRP is restricted by the growing scale of requirements. In this paper, we propose a Backbone based Multilevel Algorithm (BMA) to address the large scale NRP. In contrast to direct solving approaches, BMA employs multilevel reductions to downgrade the problem scale and multilevel refinements to construct the final optimal set of customers. In both reductions and refinements, the backbone is built to fix the common part of the optimal customers. Since it is intractable to extract the backbone in practice, the approximate backbone is employed for the instance reduction while the soft backbone is proposed to augment the backbone application. In the experiments, to cope with the lack of open large requirements databases, we propose a method to extract instances from open bug repositories. Experimental results on 15 classic instances and 24 realistic instances demonstrate that BMA can achieve better solutions on the large scale NRP instances than direct solving approaches. Our work provides a reduction approach for solving large scale problems in search based requirements engineering.

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Source: https://tomesphere.com/paper/1704.04768