用基于主干的多层算法求解大规模下一次发布问题
Solving the Large Scale Next Release Problem with a Backbone Based Multilevel Algorithm
摘要：
下一版本问题（NRP）旨在优化客户利润和软件版本的需求选择。需求规模的不断扩大制约了NRP的研究。在本文中，我们提出了一个基于骨干的多级算法（BMA），以解决大规模的NRP。与直接求解方法相比，BMA采用多级约简来降低问题的规模，并采用多级细化来构建最终的最优客户集。在缩减和细化中，主干都是为了修复最佳客户的公共部分而构建的。由于在实际应用中提取主干是一个困难的问题，本文采用近似主干进行实例约简，同时提出软主干来增强主干的应用。在实验中，针对缺乏开放的大型需求数据库的情况，提出了一种从开放的缺陷库中抽取实例的方法。在15个经典实例和24个实际实例上的实验结果表明，BMA在大规模NRP问题上的求解效果优于直接求解方法。我们的工作提供了一个约简方法，解决大规模的问题，基于搜索的需求工程。
英文摘要：
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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