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The Next Release Problem (NRP) aims to optimize customer profits andrequirements selection for the software releases. The research on the NRP isrestricted by the growing scale of requirements. In this paper, we propose aBackbone based Multilevel Algorithm (BMA) to address the large scale NRP. Incontrast to direct solving approaches, BMA employs multilevel reductions todowngrade the problem scale and multilevel refinements to construct the finaloptimal set of customers. In both reductions and refinements, the backbone isbuilt to fix the common part of the optimal customers. Since it is intractableto extract the backbone in practice, the approximate backbone is employed forthe instance reduction while the soft backbone is proposed to augment thebackbone application. In the experiments, to cope with the lack of open largerequirements databases, we propose a method to extract instances from open bugrepositories. Experimental results on 15 classic instances and 24 realisticinstances demonstrate that BMA can achieve better solutions on the large scaleNRP instances than direct solving approaches. Our work provides a reductionapproach for solving large scale problems in search based requirementsengineering.

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