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, the 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 the 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. Index Terms-The next release problem, backbone, soft backbone, multilevel algorithm, requirements instance generation, searchbased requirements engineering Ç
Because of the nature of SPL projects, new factors are involved (for example, resolving the conflicts between the requirements of core assets and the requirements of various products) REF proposes a reduction approach for solving the Next Release Problem (NPR) using the Backbone-based Multilevel Algorithm (BMA).
Solving the Large Scale Next Release Problem with a Backbone-Based Multilevel Algorithm
IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering
Computer Science